A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease

A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease A. Journey Eubank, Abhilash Thatikala, Maryam Y. Garza, Humaira Khan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7329834/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Parkinson’s disease (PD) is the second-most diagnosed age-related neurodegenerative disorder globally. PD pathology causes dysregulation of motor movement and for many, mild or minor cognitive impairment (PD-MCI). The most recommended global screening exam to detect PD-MCI is the Montreal Cognitive Assessment© (MoCA). Traditionally, the MoCA is scored according to guidelines and compared against a standardized cutoff, but clinical professionals additionally draw upon their observations of the patient’s performance to determine the score. To better understand how clinicians use the MoCA in real-world clinical settings, we employed the qualitative descriptive approach to identify performance patterns professionals utilize to assess the cognitive health of a person with PD. Methods: We curated retrospective data from nine people with PD-MCI to PD-Dementia. Each patient had one completed MoCA exam and one neuropsychological report containing health data. The assessments were organized into three groups of three and used in semi-structured interviews with six clinical professionals to gather at minimum two clinical opinions for each. Results: Three coders distilled, based on consensus, three clinically meaningful patterns from the interviews composed of features emphasized as vital by the interviewees for determining a person’s cognitive health. The derived features were from a patient’s performance on sections of the MoCA exam, sociodemographic and health data from the neuropsychological report, and dependent relationships between the assessments. Conclusions: Our study leveraged the popular MoCA exam to explore its real-world clinical use. Extracting these patterns clinicians recognized provides deeper insights into how they interpret cognitive health creating a blueprint for future efforts to tailor the exam for detecting cognitive impairment in people with PD. Health sciences/Diseases Health sciences/Health care Health sciences/Neurology Biological sciences/Neuroscience Clinical reasoning Parkinson's disease Cognitive impairment Montreal Cognitive Assessment Qualitative research Biomedical informatics Background Parkinson’s disease (PD) is a complex neurodegenerative disorder that leads to motor deficits, cognitive impairments, and neuropsychiatric symptoms, such as apathy and depression.[ 1 , 2 ] While dysregulation of movement in PD is well characterized, cognitive impairment in PD presents a unique diagnostic challenge, ranging from mild or minor (PD-MCI)[ 3 ] to major cognitive impairment (PD-Dementia)[ 4 ] as defined by the Movement Disorder Society. PD-MCI is a distinct intermediate state between PD with normal cognition and PD-Dementia. The presentations of cognitive impairment and disease progression can vary between individuals, but two cognitive subtypes are frequently recognized in the literature: one characterized by frontostriatal and executive dysfunction, often resulting in bradyphrenia, and another involving posterior cortical deficits, marked by visuospatial problems and language/semantic fluency impairments.[ 5 ] Rather than a fixed stage, PD-MCI should be thought of as a spectrum: some people remain at the PD-MCI stage for a long time, some can revert to normal cognition for a period, while others progress to dementia.[ 6 , 7 ] Notably, those who revert to normal cognition after a diagnosis of PD-MCI have an elevated risk of declining back to PD-MCI within the next year.[ 7 ] This cognitive variability is particularly relevant given that PD-MCI is identified in 20—50% of people at the time of initial PD diagnosis,[ 1 , 8 ] increasing to 40—57% after approximately 5 years of disease duration.[ 6 , 9 – 11 ] These trends underscore the importance of research focused on understanding the presentations, or patterns, of cognitive impairment in PD. The wide variability in PD-MCI prevalence at diagnoses may, in part, stem from differences in the cognitive domains assessed and psychometric properties of the exams used. This suggests a need for assessments specifically calibrated for PD-MCI. Improving the sensitivity and specificity of these tools could also aid in earlier identification of cognitive impairment in people with PD so therapeutic interventions have a greater chance of potentially slowing disease progression. The Movement Disorder Society provided guidelines for testing cognitive impairment in PD, including Level II neuropsychological exam batteries—the gold standard for diagnosis—and Level I rapid global screening exams (e.g., the Montreal Cognitive Assessment© [MoCA]).[ 12 – 14 ] Currently, when it comes to Level II diagnostic gold standards, clinical practice often faces access barriers, such as the lack of available neuropsychologists, time, cost, or a patient’s lack of insurance coverage.[ 3 ] Therefore, the Level I rapid global cognitive exams are most commonly used to gauge cognitive health.[ 3 , 15 , 16 ] The MoCA is the most widely recommended rapid global screening exam, but debates on the accuracy of the exam’s scoring method and its specificity and sensitivity for detecting PD-MCI are ongoing.[ 17 , 18 ] According to the MoCA guidelines, the total score is calculated by summing the points from each scorable section, with each cognitive domain weighted based on its assigned point value, and adding an additional point for individuals with ≤ 12 years of education.[ 19 ] The summation of the section scores is then compared to a standardized predetermined scale with ≥ 26 out of 30 indicating normal cognition. Building on this, discussions with clinicians revealed that, rather than relying solely on the MoCA total score compared to a cutoff as the determinant of cognitive health, they instead interpret patterns in a patient’s performance in the context of health-related data. These health-related contextual dependencies are of particular interest, as understanding the patterns that clinicians identify as representative of PD-MCI could lay the groundwork for developing a new method of interpreting the MoCA that improves the sensitivity and specificity for people with PD. Using semi-structured interviews and the “think aloud protocol” [ 20 , 21 ], we investigated the clinical applications of the MoCA. We sought to extract, concurrently, the clinically meaningful features of patient performance on the MoCA exam and health-related data that clinicians consider vital in determining the presence of cognitive impairment in people with PD. By distilling clinically meaningful features, we aimed to synthesize diagnostically significant patterns across the cognitive impairment spectrum in PD. Methods Study Design This study employed a descriptive qualitative methodology to explore the perspectives of a specific population.[ 22 ] Guided by the pragmatist’s paradigm, which emphasizes real-world observation through the context of a relevant but subjective source, we used purposive sampling to focus on insights from clinical neurologists and neuropsychologists specializing in movement disorders in adult and geriatric populations.[ 23 ] The Standards for Reporting Qualitative Research checklist was used to ensure transparent reporting of this study ( Supplemental 1 ).[ 24 ] This research approved by the University of Arkansas for Medical Science’s Institutional Review Board (IRB Number 274288). All procedures involving human participants followed ethical guidelines in accordance with the Declaration of Helsinki and its later amendments. Reflexivity of Researchers The interdisciplinary research team on this project provided a diverse range of expertise to guide this research and provide valuable insights into real-world clinical practice. Three experienced neurologists provided the foundational concepts and domain-specific expertise critical to the study’s conceptualization, and provided continued input throughout the project (TV, RD, HK). An experienced qualitative researcher (MYG) gave valuable guidance on qualitative methodology and trained the lead investigator in interviewing techniques. The lead investigator, (AJE, a biomedical informatics PhD candidate), an expert biomedical informaticist (FWP), and a neurology resident (AT) collaborated to extract the categories and codes to formulate the patterns from the interviews. Including a neurology resident to assist in distilling information from the interviews provided an additional valuable perspective and ensured the informaticists interpreted insights from the interviews accurately and extracted features vital to constructing clinically relevant patterns. An experienced neuroscientist and researcher (LJL-P) lent her extensive expertise in the field of PD research, offering excellent perspectives on cognitive impairment in PD. A researcher in the field of information quality (JT) provided valuable expertise on data quality practices in research to ensure rigorous and transparent data reporting. Description of Participants The Arkansas Clinical Data Repository team at the University of Arkansas for Medical Sciences curated and deidentified completed MoCA exams and neuropsychological reports from the electronic health record system, Epic, in accordance with regulatory requirements.[ 25 ] The curation criteria for the retrospective patient data for this study were: (i) Primary diagnosis of PD and a secondary diagnosis of MCI or Dementia from PD in their medical record; (ii) A completed MoCA exam and neuropsychological report in the health record. During the study design, it was determined that a total of nine patients’ MoCA exams and neuropsychological reports were optimal based on the availability of recruited clinical professionals and the number of assessments that could be reasonably reviewed in a 60-minute interview time constraint. The nine MoCA exams and neuropsychological reports were identified with the assistance from clinical colleagues in the Neurology and Geriatric Departments at the University of Arkansas for Medical Sciences. The assessments were organized into three groups of three sets of exam-report pairs (herein referred to as patient profiles). The neuropsychological reports contained a comprehensive overview of the patient, describing presenting concerns, biopsychosocial history, psychiatric symptoms, activities of daily living, neurobehavioral status, cognitive domain test results, clinical impressions, diagnostic summary, and recommendations from the time of the neuropsychological appointment. Notably, while the health information was current at the neuropsychological appointment, the completed MoCA exam in their file could have been administered prior to or after the time of the neuropsychological appointment date. Table 1 provides the administration date for both assessments and the final diagnosis by patient ID number. The assessments were completed with a mean difference of 1.70 years (SD ≈ 1.38 years). Despite this time gap, the report generated from the neuropsychological appointment was a vital assessment that succinctly provided pertinent health information traditionally available if the interviewee were the patient’s clinical specialist, thereby simulating real-world clinical practice. In our retrospective data set, three patients were diagnosed with PD with mild cognitive impairment non-amnestic (PD-MCI non-A), two were categorized as amnestic mild cognitive impairment (PD-MCI A), and two were classified as PD-Dementia (Table 1 ). Table 1 The Recorded Date of Administration for the Assessments and Final Diagnosis by Patient ID Number Patient ID MoCA Date Neuropsychological Report Date Final Diagnosis 1 4/2016 4/2014 Dementia due to PD 2 1/2016 4/2015 Mild cognitive impairment (non-amnestic) 3 10/2015 6/2014 Amnestic mild cognitive impairment 4 11/2019 9/2014 Amnestic mild cognitive impairment 5 11/2015 8/2015 Mild cognitive impairment (non-amnestic) 6 4/2021 11/2018 Dementia due to PD 7 11/2014 7/2015 Mild cognitive impairment (non-amnestic) 8 10/2019 1/2021 Mild cognitive impairment (non-amnestic) 9 6/2021 12/2019 Dementia due to PD All potential interviewees were provided a brief overview of the research purpose and participant protections at the time of recruitment to review upon deciding to participate, thus acknowledging they understood the benefits and risks of participation. A total of six clinical professionals were recruited, clinical neurologists (n = 5) and a neuropsychologist (n = 1), from two different hospital systems to participate in the interviews and ensure we gathered, at minimum, two opinions on each patient profile. The recruitment criteria required potential participants to be currently practicing clinical neurologists or neuropsychologists specializing in movement disorders in the adult to geriatric population. The advising neurologist (RD), the senior author and biomedical informatics expert (FWP), and the lead investigator (AJE) recruited potential interviewees by corresponding with colleagues, associates, and mentors. Data Collection Verbal informed consent was obtained from all volunteer interviewees prior to beginning the interview recording. The interviewees received their three assigned patient profiles one week in advance and were asked to review them prior to the interview. The semi-structured interviews were formatted using the think aloud protocol,[ 21 ] an interview technique designed to study thought processes, which has previously been leveraged to gain insights into medical decision-making for diagnostic techniques and investigations into the clinical understanding of cognitive impairment.[ 26 – 28 ] The think aloud protocol requires the interviewee to speak their thoughts aloud as they perform a specific task, thereby providing a verbal account of their critical thinking and decision-making processes. The interviewer (AJE) utilized an interview guide developed by research team members (AJE, FWP, RD, MYG) ( Supplemental 2 ). The interview questions were developed using an iterative approach and pilot-tested with an advising clinician (RD) to determine applicability. The purpose of the interview guide was to assist the interviewer in encouraging the interviewees to articulate their observations as they reviewed their three assigned patient profiles. The questions focused on the patterns of patient performance on the MoCA exam, what important health features an interviewee would want to know from the neuropsychological report, and how they interpreted any context dependence between the two assessments and if they agreed with the diagnosis of the patient. For example, the question, “What do you observe/look for in the patient’s performance?” was used in the MoCA section to prompt detailed descriptions of clinicians’ observation and interpretive practices when reviewing the exam. A similar question was used in the neuropsychology report portion of the guide: “What do you look for in this section? What does its presence/absence mean to you?” To continually prompt the interviewee to verbalize their thought processes as they reviewed the documents this question was used: “Could you talk me through the aspects of the narrative that helped you determine that?” Additionally, the guide functioned as a reference for the interviewer to keep the discussion focused and within the allotted time frame. To further simulate real-time clinical practice procedures, the interviewees were instructed to share their computer screens during the interview, allowing them to review the patient profiles freely according to their workflow methodology. A trained qualitative researcher (AJE) conducted interviews via videoconference lasting ≤ 60 minutes. The interviews were recorded and stored on a HIPAA-compliant platform. All interviews were transcribed verbatim by AJE. To ensure interviewee privacy protections, personal identifiers in the transcripts were deidentified using an alphanumeric code prior to sharing with colleagues contributing to the interview coding process. Data Processing and Analysis Recurrent themes in the data were identified by inductive and deductive hybrid thematic analysis.[ 29 , 30 ] The adaptive approach allowed inductive elements to emerge by identifying key features essential to the differential diagnostic process. Simultaneously, deductive reasoning ensured a focus on the research question: whether the features noted as important for determining cognitive health by the interviewees formed diagnostically meaningful patterns of cognitive impairment in PD. As this study was largely explorative, features of importance were extracted by recursive open coding of the transcripts,[ 31 ] which allowed for refinement and triangulation as the interviews progressed to enhance the rigor of the deductive and inductive content analysis and subsequent extraction of patterns.[ 31 , 32 ] Table 2 displays an example of the methods used to derive important features underlying the codes and categories from interview quotes that reflecting clinicians’ observations. The table shows the document the interviewee was discussing while making an observation about patient performance on the MoCA, relationships between MoCA performance and health-related data from the neuropsychological report, and other clinically relevant details from the report that may impact MoCA performance. Table 2 Example Quotes with Associated Codes and Categories from Qualitative Analysis Quote Code Category Document Important Feature(s) “I mean we always have their med list and medical history and things like that…” Use of medical background Contextual health data for cognitive assessment Neuropsychological report Medication list, medical history “So gives me a little bit of insight, you know, years of education, what they did for a living, you know who their family is and who their support system is and that sort of thing. So, so I find those things to be…helpful to kind of put everything into context.” Contextual patient background Biopsychosocial contextualization Neuropsychological report Years of education, occupational history, family support system “…when I moved down to the kind of registration part of the memory looks like it took. A few tries to get it correct, looks like they may have had some difficulty with hearing when they said faith instead of face.” Interpreting test behavior through sensory/context cues Exam performance contextualization MoCA exam Number of tries to get MoCA memory task correct, hearing loss The data was input into Microsoft Excel, using a row per patient profile (n = 9), grouping features of importance from the assigned interviewees to establish one pattern per patient profile. Features were deemed important based on collaborative discussions between AJE and FWP, as they reviewed the interview transcripts creating codes and categories according to their interpretations of what the interviewee pointed out as diagnostically meaningful. A neurology resident (AT) was recruited to review the interview transcripts independently, extract features of importance and organize these features into patterns for each patient profile as AJE and FWP had previously done. Meetings between the three interview coders (AJE, FWP, AT) were held to reach a consensus on each patient profile’s important features and respective pattern. Then, all three interview coders independently reviewed each pattern to determine if any could be combined using these devised pattern reduction rules: (i) are there clinically meaningful differences and, (ii) can the features be quantified? Clinical meaningfulness was based on the frequency a specific feature was mentioned in an interview and its co-occurrence with other features. Only features that could be quantitively represented—either using a binary indicator for presence (1) or absence (0), or numerically (e.g., structural similarity index of the cube example and patient drawing from the MoCA)—were retained. A consensus meeting was held to finalize the list of patterns. To statistically compare patient profile demographics and cognitive exam scores between the three independent groups (PD-MCI non-A, PD-MCI A, PD-Dementia) the Kruskal-Wallis test was used. This test is preferred for small sample sizes—requiring only two people per group—and it does not assume normality or equal variance, which is appropriate given our unequal and small group sizes. Results Table 3 displays the patient demographics and the Kruskal-Wallis statistical test effect sizes for the patient’s age, number of years of education, total MoCA score and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)[ 33 ] total index score. The nine patient profiles were divided into three groups according to the diagnostic impression in the neuropsychological report (PD-MCI non-A, PD-MCI A, PD-Dementia). The Kruskal-Wallis Test revealed a significant difference in RBANS scores across diagnostic groups (H (2) = 6.11, p = .047, η² = 0.76). Post-hoc Dunn’s test with Bonferroni correction showed that the PD-MCI non-A group scored significantly higher than the PD-Dementia group (adjusted p -value = 0.043), which is expected as a higher score suggests better cognitive functioning. Table 3 Patient Profile Demographics, Years of Education, MoCA and RBANS Total Scores Groups Compared H Statistic p-value η² (Effect Size) Interpretation Age PD-MCI non-A, PD-MCI A, PD-Dementia 1.99 0.369 0.249 Small Years of Edu. 2.63 0.268 0.329 Medium MoCA 4.90 0.086 0.613 Large RBANS 6.11 0.047 ⱡ 0.764 Large Table 3 : Interpretation thresholds for η²: Small ≈ 0.01, Medium ≈ 0.06, Large ≥ 0.14; ⱡ p < 0.05. During an interview, it was discovered that the performance of patient ID 6 on the MoCA exam and cognitive assessment battery in the neuropsychological report was most consistent with a diagnosis of delirium rather than impairment due to PD. This resulted in removal of this patient profile, as it could not be definitively determined that the patient’s performance on the MoCA exam was unaffected by delirium, thereby reducing the number of patient profiles to eight (n = 8). Patient ID 1 was added to the affected group of three patient profiles to supplement the deficit, resulting in three clinical opinions for this patient profile. Additionally, one of the two clinicians that reviewed Patient ID 8 expressed that they disagreed with the diagnosis of PD-MCI non-A recorded in the neuropsychological assessment report. Instead, they suggested that the patient’s performance on the MoCA and extent of affected activities of daily living recorded in the neuropsychological report reflected mild PD-Dementia. Table 2 illustrates how codes and categories were derived to capture specific features that clinicians considered necessary for their differential diagnostic processes as they reviewed the patient profiles. The ‘Important Feature(s)’ column presents the synthesized features from interview quotes, reflecting how clinicians integrated medical history (e.g., medication lists), contextual health features that influence MoCA performance (e.g., hearing issues), and broader personal information (e.g., presence of family support) to interpret the patient’s cognitive functioning beyond the MoCA total score. An example of how we used qualitative methods to synthesize the important features composing the patterns represented in Table 4 can be seen by directing your attention to the third quote in Table 2 . This quote shows how a clinical professional utilized contextual patient health information to interpret performance on the MoCA. The quote is extracted from an interview where the clinician was reviewing the Memory subtask of the MoCA exam and noted that when the patient was supposed to repeat the word ‘face’ instead presumably said ‘faith’, per a note on the exam document. This note prompted the interviewee to consider if the patient had a hearing impairment. This important feature is reflected in Pattern 3 of Table 4 in the ‘MoCA Section’ column noted as ‘Memory: number of tries, correlate to hearing issue’. Several features were repeatedly identified by the majority of interviewees as clinically relevant to interpreting a patient’s performance on the MoCA or important contextual health information. Consequently, these features appeared in all three final patterns and are recorded in the ‘Recurrent Features’ row of Table 4 . The repetition reflects consistent clinical relevance based on the patter reduction criteria described above, demonstrating that pattern features are not mutually exclusive. In the ‘MoCA Section’ column of Table 4 , there are several correlations mentioned between a specific cognitive domain section and health-related data from the neuropsychological report. The Language and Attention feature in Pattern 1 of the ‘MoCA Section’ column, includes checking performance for lexical fluency. Lexical capabilities are often diminished when cognitive functions are degraded by Alzheimer’s disease pathology, cardiovascular issues,[ 34 ] or vascular risk factors,[ 35 ] making these critical contextual health features to check for and are noted within the ‘Health Data’ column. In Pattern 2, a feature in the Attention and Language domain sections in the ‘MoCA Section’ column notes that task performance must be normalized by education. This feature points to the importance of contextual biopsychosocial factors, as individuals with higher educational degrees tend to perform better on these tasks.[ 36 ] In Pattern 3, the Memory feature listed in the ‘MoCA Section’ column illustrates how a clinician might suspect that a physical health-related feature is impacting exam performance. To complete the Memory subsection of the MoCA exam, patients must listen to words said by the exam administrator and repeat them back; however, if a patient has hearing aids or a hearing impairment and performs poorly, the results may reflect a physical limitation rather than a cognitive dysfunction. Similarly, in the Clock task of the ‘MoCA Section’ within Patterns 2 and 3, one feature notes the small size of the analog clock numbers, referred to in the literature as ‘micrographia’, a sequela of PD commonly linked to motor dysfunction.[ 37 ] While this may appear irrelevant for understanding presentations of the cognitive impairment spectrum in PD, patterns are produced by the extraction of features of importance that clinical professionals recognized while interpreting the assessments. The patterns in Table 4 offer a holistic perspective of patient health derived from the eight model patient profiles used to create them. Therefore, we did not eliminate features involving motor symptoms as they were determined to be clinically meaningful because they were verbalized by the clinical professionals. Table 4 Clinically Meaningful Features from the MoCA exam and Neuropsychological Report MoCA Exam Section Neuropsychological Report Recurrent features in all patterns: Trail Task : Evidence of errors (e.g., “Any errors”) Cube : Difference from ideal cube (e.g., 3D-ness) - Medication list - Sleep disorder/issues - Impaired independence of activities of daily living - Years of education Pattern 1: Trail Task : Number of errors Cube : Jitter in lines, degree of skew Clock : Numbers not sequential or missing, no short/long hand, position of hands, contour Language and Attention : Lexical fluency and serial 7 ( Attention ) tasks- check lexical fluency for Alzheimer’s disease, the total number of words given in lexical fluency task - Hearing loss/aids - Vision loss - B12 deficiency - Evidence of delirium - Vascular risk factors - Activity level - Premorbid level of functioning Pattern 2: Trail Task : Correction of errors (extra lines) Cube : Jitter in lines Clock : Small size of numbers, evidence of correction of error (extra lines) Attention : Serial 7s and letter vigilance task performance (normalized by years of education), Attention test: note early stopping on letter vigilance task, delayed recall section, or lexical fluency task ( Language ) Language : Number of words on lexical fluency task normalized by years of education - Medication specifics (cognition-impacting drugs) - Occupational exposure to pesticides - Evidence of urinary tract infection - Family history of dementia - History of concussion(s) Pattern 3: Trail Task : Level of completion, jitter of lines Cube : size, shape, orientation Clock : Number of tries, percent complete, size of circle and numbers, contour, orientation, number spacing/order, position and length of clock hands Memory : Number of tries, correlate to hearing issue Language and Attention : Lexical fluency and serial 7 task ( Attention ) (normed by years of education) correlate with attention issues Delayed Recall : Categorical vs. multiple choice cue success - Medication specifics (PD medication timing, sedatives, dementia drugs) - Vision loss - Hearing loss/aids - Arthritis - Family support - Vascular risk factors - Cardiovascular disease indicators - Premorbid level of functioning - Social engagement Discussion Our objective was to distill diagnostically meaningful patterns of cognitive impairment in people with PD by examining how clinicians interpreted the MoCA in conjunction with patient health data. The interviews with clinical experts revealed the discrepancy between the currently recommended MoCA scoring method and its clinical application. Each section of the MoCA exam includes a checkbox for task completion and the exam instructions advise the proctor to award or deduct points based on specific criteria. For example, giving a point if the patient drew relatively parallel lines for reproducing a 3D cube in the Cube sub-section, or deduct a point for an incorrect word recall in the Delayed Recall section. This research supported the insights shared by clinical mentors during the initial conceptualization of the study, that clinicians move beyond adherence to the MoCA scoring instructions. Instead, relying on pattern recognition—an integral part of differential diagnostics—to interpret patterns of patient performance on the MoCA exam in relation to health features. Our results, summarized in Table 4 illustrate these important features that clinicians see as diagnostically vital, such as noting the presence of arthritis—noted in Pattern 3—which could impact their drawing of a 3D cube, or if the patient is taking medications that could impair memory capacity—noted in Pattern 2.[ 30 , 38 ] This is particularly notable given that medications used to treat the motor and non-motor symptoms in PD can cause side effects like sleep disturbances, drowsiness, or memory problems.[ 39 ] Additionally, many people with PD experience anxiety, depression, and apathy, which can affect their performance on global screening exams like the MoCA, further highlighting the importance of integrating contextual health information into exam scoring procedures.[ 40 ] These relationships are typically considered implicitly by clinicians during exam interpretation; however, our analysis makes this diagnostic reasoning explicit. Other rapid screening tools with comparable administration time to the MoCA exam such as the Parkinson’s Disease-Cognitive Rating Scale,[ 41 ] the Scales for Outcomes of Parkinson’s Disease,[ 42 ] or the Parkinson Neuropsychometric Dementia Assessment[ 43 ] are specifically designed for detecting cognitive deficits in people with PD but are not commonly employed in the clinical setting. The assimilation of new tools in the clinic is often lengthy and challenging, as evidenced by the high failure rate of clinical decision support systems.[ 44 ] This difficulty is primarily due to the disruptive impact on clinical workflows, compounded by the need for training and certifications necessary for successful implementation. Following this evidence, a similar challenge would likely arise with the introduction of a new global screening tool, which may help explain the reason why PD-specific screening exams are not as widely used as the MoCA. This reasoning prompted the selection of the MoCA exam as the model global screening tool for this study. Additionally, we believe that optimizing an established and popular global screening exam would provide the foundation for a more rapid acceptance of any future recommended scoring alterations. Our research has several limitations that must be acknowledged. The first is the small sample size of both interviewees (n = 6) and the number of patient profiles (n = 8). The small sample size of interviewees was a result of limited access to clinical neurologists and neuropsychologists in the home institution the study was conducted. Five interviewees were recruited from the study’s home institution, and one interviewee was recruited from an external healthcare institution. Our cohort had five clinical neurologists, who are considered experts in their respective fields of neurology and one neuropsychologist who sees many patients with movement disorders. Recruitment of only one neuropsychologist was due to limited availability of these specialized professionals. Particularly those who frequently work with older adults with movement disorders. Additionally, it must be mentioned that this study’s premise is to explore patterns of patient performance on the MoCA in relation to their health data. Neuropsychologists are experts in curating cognitive batteries specifically tailored for analysis of discrete aspects of a patient’s cognition and do not traditionally utilize the MoCA exam as a component of their battery. For this reason, while prudent to include a neuropsychologist’s opinion given their insights into the neuropsychological effects of a neurodegenerative disorder such as PD, it was not necessary to recruit additional participants. Furthermore, inductive thematic saturation was achieved during analysis of the interview transcripts as we used purposive sampling to gather a homogenous expert population of clinical professionals specializing in movement disorders and geriatrics. The small sample size of patient profiles (n = 9) was based on maximizing the number of patient assessments that could be reasonably reviewed within the 60-minute interview time limit leading to the to the conclusion that reviewing three patient profiles per interview would be practical. Another limitation is that the final number of patterns (n = 3) is small. However, these patterns emerged through thematic saturation based on the recurrence of specific features across all interviews and the frequency with which certain features appeared together. Arguably, a larger number of patterns could be extracted by decomposing these three patterns, but focusing on a smaller set of distilled patterns offers a more targeted approach to identifying whether these patterns can be meaningfully detected in a larger dataset. This focused approach enhances our ability to explore how clinical professionals interpret patient performance on the MoCA and health-related data when mining for differential diagnostic insights. We also would like to acknowledge that one of the primary purposes of the MoCA is to track longitudinal changes in cognition. The exam’s inclusion of a broad range of cognitive domains, especially visuospatial executive functions which are commonly affected by PD progression, makes it a valuable screening tool. However, for the purposes of this study, a single time point was sufficient to capture the important features clinicians note during their differential diagnostic processes. An additional limitation was the time frame between when the MoCA exam was completed, and when the neuropsychological report was created. It could be reasonably argued that the time gap affected correlations between the patient’s performance on the MoCA exam and their health data recorded in the report. The majority of the MoCA exams and neuropsychological reports were completed with a mean of 1.70 years (SD ≈ 1.38 years) of each other, apart from Patient ID 4, who had 5 years and 2 months between (Table 1 ). However, it should be noted that we were extracting features that are clinically relevant for assessing the performance on the MoCA exam by people with PD on the cognitive impairment spectrum—as noted in the ‘Final Diagnosis’ column of Table 1 —rather than determining the presence of cognitive impairment. The purpose of presenting the three different patient profiles with a wide range of PD symptomology to each interviewee was to ensure we prompted multiple different contexts and elicited complex pattern recognition skills. Conclusion Patterns in health data have previously been leveraged to differentiate the diverse presentations of PD. Imaging studies have successfully distinguished idiopathic PD from other similarly presenting degenerative disorders, such as progressive supranuclear palsy or related parkinsonisms.[ 45 ] Other research has also described the utility of a patient’s health history and physical exams in identifying clinical patterns of disease-causing pulmonary dysfunction.[ 46 , 47 ] Through this lens, we can extrapolate that when patient health-related data is considered, there are discriminating disease features to be gleaned. Unfortunately, reliable and robust detection of cognitive deficits in PD remains a persistent challenge. This is particularly concerning given that PD-MCI reduces patient quality of life, is a risk factor for developing dementia, and increases the risk of early mortality.[ 1 , 9 , 13 , 48 ] In the absence of preventative treatments for cognitive impairment in PD, and with the estimated prevalence of PD increasing globally, the need to develop robust screening exams for early detection is urgent.[ 49 – 53 ] We argue that by leveraging the innate human capacity for pattern recognition,[ 30 , 54 ] which is the grounded authority of the differential diagnostic process,[ 27 ] we can begin to decipher clinically relevant disease patterns within the complex symptomology of the cognitive impairment spectrum in PD. This insight could optimize cognitive exams for PD, thereby facilitating earlier identification and more timely introduction of interventions, such as exercise, to slow disease progression.[ 55 ] Extracting diagnostically meaningful patterns of cognitive impairment was the first step toward achieving that goal. The next is to quantitatively represent the extracted pattern features and determine if they exist outside of this sample data set in a large corpus of MoCA exams and patient health data. Abbreviations HIPPA Health Insurance Portability and Accountability Act PD Parkinson’s Disease PD Dementia—Parkinson’s Disease with Major Cognitive Impairment PD MCI—Parkinson’s Disease with Mild (Minor) Cognitive Impairment PD MCI A—Parkinson’s Disease with Mild Cognitive Impairment Amnestic PD MCI non—A—Parkinson’s Disease with Mild (Minor) Cognitive Impairment Non—Amnestic RBANS Repeatable Battery for the Assessment of Neuropsychological Status SD Standard Deviation Declarations Ethics Approval and Consent to Participate: The University of Arkansas for Medical Science’s Institutional Review Board approved the study (IRB Number 274288). All human participants provided verbal informed consent prior to the start of the interview recording. All research procedures were conducted in accordance with the ethical standards of the home intuition of the study and with the Declaration of Helsinki and its later amendments. Consent for Publication: Not applicable. Availability of Data and Materials: The datasets generated and/or analyzed during the current study are available in the Zenodo repository, the interview transcripts (DOI: 10.5281/zenodo.16861070), and the Excel document describing how the patterns were derived from the interviews (DOI: 10.5281/zenodo.16859473). Competing Interests: The authors declare that they have no competing interests with respect to the research, authorship, and/or publication of this article. Funding: This material is based upon work supported by the National Science Foundation under Award No. OIA-1946391. Additionally, the research reported here is partially supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UM1 TR004909. The content of this original research is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the National Institutes of Health. Authors’ Contributions: AJE and FWP led the study design with foundational conceptual support from TV, RD, and HK. MYG trained AJE in qualitative research methodology and reviewed the study protocol for accuracy. AJE conducted all interviews and authored transcripts. AT, FWP, and AJE extracted features from transcripts to create patterns. FWP, LJL-P, JT, MYG, TV, RD, and HK reviewed and critiqued the manuscript. All authors were responsible for the decision to submit the manuscript for publication. Acknowledgments: We thank all the interviewees who volunteered for our study, without whom this research would not have been possible. Likewise, we would like to thank M. Denise Compton, PhD, who donated her time to collaborate with our clinical colleagues to identify the patient profiles. We also thank the Arkansas Clinical Data Repository team, especially Kim D. Gates, BBA, who led the curation and deidentification efforts. The authors would also like to thank MoCA Test Inc.© (All rights reserved) for allowing us to use the MoCA exam for this academic research. We also acknowledge the support provided during preparation of this manuscript made possible by the Scholarly Writing Program of the Center for Childhood Obesity Prevention funded by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM109096 (Arkansas Children’s Research Institute, PI: Borsheim). References Baiano C, Barone P, Trojano L, Santangelo G. Prevalence and clinical aspects of mild cognitive impairment in Parkinson’s disease: A meta-analysis. Movement Disorders. 2020;35:45–54. Weintraub D, Caspell-Garcia C, Simuni T, Cho HR, Coffey CS, Aarsland D, et al. Neuropsychiatric symptoms and cognitive abilities over the initial quinquennium of Parkinson disease. Ann Clin Transl Neurol. 2020;7:449–61. Litvan I, Goldman JG, Tröster AI, Schmand BA, Weintraub D, Petersen RC, et al. Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Movement Disorders. 2012;27:349–56. Kulisevsky J, Litvan I, Weintraub D, Goldman JG, Tröster AI, Lewis SJG, et al. A Call for Change: Updating the Operational Definition for Dementia in Parkinson’s Disease. Mov Disord Clin Pract. 2025;12:296–301. Goldman JG, Vernaleo BA, Camicioli R, Dahodwala N, Dobkin RD, Ellis T, et al. Cognitive impairment in Parkinson’s disease: a report from a multidisciplinary symposium on unmet needs and future directions to maintain cognitive health. NPJ Parkinsons Dis. Nature Publishing Group; 2018. Aarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, et al. Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers. 2021;7. Jones JD, Kuhn TP, Szymkowicz SM. Reverters from PD-MCI to cognitively intact are at risk for future cognitive impairment: Analysis of the PPMI cohort. Parkinsonism Relat Disord. 2018;47:3–7. Santangelo G, Vitale C, Picillo M, Moccia M, Cuoco S, Longo K, et al. Mild Cognitive Impairment in newly diagnosed Parkinson’s disease: A longitudinal prospective study. Parkinsonism Relat Disord. 2015;21:1219–26. Saredakis D, Collins-Praino LE, Gutteridge DS, Stephan BCM, Keage HAD. Conversion to MCI and dementia in Parkinson’s disease: a systematic review and meta-analysis. Parkinsonism Relat Disord. Elsevier Ltd; 2019. p. 20–31. Castelli MB, Alonso-Recio L, Carvajal F, Serrano JM. Does the Montreal Cognitive Assessment (MoCA) identify cognitive impairment profiles in Parkinson’s disease? An exploratory study. Applied Neuropsychology:Adult. 2024;31:238–47. Broeders M, De Bie RMA, Velseboer DC, Speelman JD, Muslimovic D, Schmand B. Evolution of mild cognitive impairment in Parkinson disease [Internet]. 2013. Available from: www.neurology.org Postuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, et al. MDS clinical diagnostic criteria for Parkinson’s disease. Movement Disorders. John Wiley and Sons Inc; 2015. p. 1591–601. Litvan I, Aarsland D, Adler CH, Goldman JG, Kulisevsky J, Mollenhauer B, et al. MDS task force on mild cognitive impairment in Parkinson’s disease: Critical review of PD-MCI. Movement Disorders. 2011;26:1814–24. Nasreddine ZS, Phillips NA, Bédirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695–9. Auclair-Ouellet N, Mandl S, Kibreab M, Haffenden A, Hanganu A, Cheetham J, et al. Characterization of cognition in mild cognitive impairment with and without Parkinson’s disease. Clin Park Relat Disord. 2020;3. Rosenblum S, Meyer S, Gemerman N, Mentzer L, Richardson A, Israeli-Korn S, et al. The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson’s Disease? Mov Disord Clin Pract. 2020;7:648–55. Dalrymple-Alford J, MacAskill M, Nakas C, Livingston L, Graham BC, Crucian MG, et al. The MoCA Well-suited screen for cognitive impairment in Parkinson disease [Internet]. Neurology ®. 2010 p. 1717–25. Available from: www.mocatest.org Rosenblum S, Meyer S, Gemerman N, Mentzer L, Richardson A, Israeli-Korn S, et al. The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson’s Disease? Mov Disord Clin Pract. 2020;7:648–55. Fengler S, Kessler J, Timmermann L, Zapf A, Elben S, Wojtecki L, et al. Screening for cognitive impairment in Parkinson’s disease: Improving the diagnostic utility of the MoCA through subtest weighting. PLoS One. 2016;11. Nielsen J, Clemmensen T, Yssing C. Getting access to what goes on in people’s heads?-Reflections on the think-aloud technique. Proceedings of the second Nordic conference on Human-computer interaction. 2002;101–10. Jääskeläinen R. Think-aloud protocol. In: Gambier Y, Doorslaer van L, editors. Handbook of translation studies. John Benjamins publishing company; 2012. p. 371–3. Bradshaw C, Atkinson S, Doody O. Employing a Qualitative Description Approach in Health Care Research. Glob Qual Nurs Res. 2017;4. Allemang B, Sitter K, Dimitropoulos G. Pragmatism as a paradigm for patient-oriented research. Health Expectations. John Wiley and Sons Inc; 2022. p. 38–47. O’Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine. 2014;89:1245–51. Baghal A, Zozus M, Baghal A, Al-Shukri S, Prior F. Factors associated with increased adoption of a research data warehouse. Stud Health Technol Inform. 2019;257:31–5. Blair EM, Zahuranec DB, Forman J, Reale BK, Langa KM, Giordani B, et al. Physician Diagnosis and Knowledge of Mild Cognitive Impairment. Journal of Alzheimer’s Disease. 2022;85:273–82. Harold C. Sox, Michael C. Higgins, Douglas K. Owens. Differential Diagnosis. Medical Decision Making. Second. Chichester, UK: John Wiley & Sons, Ltd; 2013. p. 7–26. Wolcott MD, Lobczowski NG. Using cognitive interviews and think-aloud protocols to understand thought processes. Curr Pharm Teach Learn. 2021;13:181–8. Fereday J, Adelaide N, Australia S, Eimear Muir-Cochrane A. Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. 2006. Schell D. Pattern Recognition as an Indicator of Diagnostic Expertise. Algarve; 2013 Feb. Babchuk WA. Fundamentals of qualitative analysis in family medicine. Fam Med Community Health. 2019;7:e000040. Seers K. Qualitative data analysis. Evid Based Nurs. 2012. p. 2. Randolph C, Tierney MC, Mohr E, Chase TN. Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [Internet]. 1998. Available from: http://www.harcourt-uk.com/product. Grodstein F. Cardiovascular risk factors and cognitive function. Alzheimer’s and Dementia. 2007;3. Camerino I, Ferreira J, Vonk JM, Kessels RPC, de Leeuw FE, Roelofs A, et al. Systematic Review and Meta-Analyses of Word Production Abilities in Dysfunction of the Basal Ganglia: Stroke, Small Vessel Disease, Parkinson’s Disease, and Huntington’s Disease. Neuropsychol Rev. Springer; 2024. p. 1–26. Lin F, Jia W, Li X, Chen Y, Wan M. Cognitive Profiles Stratified by Education Using Montreal Cognitive Assessment in Parkinson’s Disease Patients with Freezing of Gait. Neuropsychiatr Dis Treat. 2024;20:25–34. Eklund M, Nuuttila S, Joutsa J, Jaakkola E, Mäkinen E, Honkanen EA, et al. Diagnostic value of micrographia in Parkinson’s disease: a study with [123I]FP-CIT SPECT. J Neural Transm. 2022;129:895–904. Elstein AS, Schwarz A. Evidence base of clinical diagnosis. Clinical problem solving and diagnostic decision making: Selective review of the cognitive literature. Br Med J. BMJ Publishing Group; 2002. p. 729–32. Marras C, Tröster AI, Kulisevsky J, Stebbins GT. The tools of the trade: A state of the art ‘How to Assess Cognition’ in the patient with Parkinson’s disease. Movement Disorders. 2014;29:584–96. Pirogovsky-Turk E, Moore RC, Filoteo JV, Litvan I, Song DD, Lessig SL, et al. Neuropsychiatric Predictors of Cognitive Decline in Parkinson Disease: A Longitudinal Study. American Journal of Geriatric Psychiatry. 2017;25:279–89. Pagonabarraga J, Kulisevsky J, Llebaria G, García-Sánchez C, Pascual-Sedano B, Gironell A. Parkinson’s disease-cognitive rating scale: A new cognitive scale specific for Parkinson’s disease. Movement Disorders. 2008;23:998–1005. Marinus J, Visser M, Verwey N, Verhey F, Middelkoop H, Stiggelbout A, et al. Assessment of cognition in Parkinson’s disease. Neurology [Internet]. 2003;61:1222–8. Available from: www.neurology.org Kalbe E, Calabrese P, Kohn N, Hilker R, Riedel O, Wittchen HU, et al. Screening for cognitive deficits in Parkinson’s disease with the Parkinson neuropsychometric dementia assessment (PANDA) instrument. Parkinsonism Relat Disord. 2008;14:93–101. Kilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis. Int J Med Inform. 2017;98:56–64. Tang CC, Poston KL, Eckert T, Feigin A, Frucht S, Gudesblatt M, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol [Internet]. 2010;9:149. Available from: www.thelancet.com/neurology Al-Ashkar F, Mehra R, Mazzone PJ. Interpreting pulmonary function tests: Recognize the pattern, and the diagnosis will follow. Cleve Clin J Med. 2003;70:866–81. McMahon L, Blake C, Lennon O. A systematic review and meta-analysis of respiratory dysfunction in Parkinson’s disease. Eur J Neurol. John Wiley and Sons Inc; 2023. p. 1481–504. Wen MC, Chan LL, Tan LCS, Tan EK. Mild cognitive impairment in Parkinson’s disease: A distinct clinical entity? Transl Neurodegener. 2017;6. Seppi K, Ray Chaudhuri K, Coelho M, Fox SH, Katzenschlager R, Perez Lloret S, et al. Update on treatments for nonmotor symptoms of Parkinson’s disease—an evidence-based medicine review. Movement Disorders. John Wiley and Sons Inc; 2019. p. 180–98. Chandler JM, Nair R, Biglan K, Ferries EA, Munsie LM, Changamire T, et al. Characteristics of Parkinson’s Disease in Patients with and without Cognitive Impairment. J Parkinsons Dis. 2021;11:1381–92. Willis AW, Roberts E, Beck JC, Fiske B, Ross W, Savica R, et al. Incidence of Parkinson disease in North America. NPJ Parkinsons Dis. 2022;8. Mmed Z, Cui Mmed YJ, Mmed Z, Mmed Y, Md S, Feng T, et al. Temporal trends in the prevalence of Parkinson’s disease from 1980 to 2023: a systematic review and meta-analysis [Internet]. Articles Lancet Healthy Longev. 2024. Available from: www.thelancet.com/ Ray Dorsey E, Elbaz A, Nichols E, Abd-Allah F, Abdelalim A, Adsuar JC, et al. Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17:939–53. Mattson MP. Superior pattern processing is the essence of the evolved human brain. Front Neurosci. 2014; Cammisuli DM, Cammisuli SM, Fusi J, Franzoni F, Pruneti C. Parkinson’s Disease–Mild Cognitive Impairment (PD-MCI): A Useful Summary of Update Knowledge. Front Aging Neurosci. Frontiers Media S.A.; 2019. Additional Declarations No competing interests reported. Supplementary Files Supplemental1.docx Supplemental2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 29 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 11 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 03 Sep, 2025 Editor invited by journal 17 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7329834","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":553525148,"identity":"10c31626-98be-453f-a0bf-f9144fe5780b","order_by":0,"name":"A. Journey Eubank","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFCCAwzMQNKAH8plbCBai2QD8VoYIFoMDhCrhb/x8DHpgpo7xsY3kp9u/MFgI7vhAAEtEgeOpUnPOPbMzOxGmtltHoY0Y4JaGA6cMZPmYTtsY3Yjwew2A8PhRIJa5A+c/ybN8++wjfGM9G83fzD8J6zF4MAZNmnetsNmBhI5Zjd4GA4Q1mJ44Jix9cy+w8YSZ96U3eYxSDaeSUiL3I3DD28XfDts2N+evu3mjwo72T5CWoBBhuJOQspBgL+BGFWjYBSMglEwogEAOG9Kz2MTIUEAAAAASUVORK5CYII=","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"A.","middleName":"Journey","lastName":"Eubank","suffix":""},{"id":553525151,"identity":"febb5845-8072-47f3-920e-e39b50f029c8","order_by":1,"name":"Abhilash Thatikala","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abhilash","middleName":"","lastName":"Thatikala","suffix":""},{"id":553525153,"identity":"0d2bd80c-79c2-4b10-a982-d8fd2549fb70","order_by":2,"name":"Maryam Y. Garza","email":"","orcid":"","institution":"The University of Texas Health Science Center at San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"Y.","lastName":"Garza","suffix":""},{"id":553525155,"identity":"733718e1-b5bd-416d-9cd8-caeb88b92fff","order_by":3,"name":"Humaira Khan","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Humaira","middleName":"","lastName":"Khan","suffix":""},{"id":553525156,"identity":"46abba6a-92c2-41d1-bd3b-103fc721c33b","order_by":4,"name":"Tuhin Virmani","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tuhin","middleName":"","lastName":"Virmani","suffix":""},{"id":553525157,"identity":"a7f7dabf-36bb-470a-8855-f442181526dc","order_by":5,"name":"Rohit Dhall","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Dhall","suffix":""},{"id":553525160,"identity":"30788132-db97-46c5-8901-c91a7f1f4e2c","order_by":6,"name":"John Talburt","email":"","orcid":"","institution":"University of Arkansas at Little Rock","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Talburt","suffix":""},{"id":553525163,"identity":"3ba1229d-ee71-4a73-a88f-a690cba65d12","order_by":7,"name":"Linda J. Larson-Prior","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Linda","middleName":"J.","lastName":"Larson-Prior","suffix":""},{"id":553525164,"identity":"ba27c064-469d-47cf-8f72-8ddef412031b","order_by":8,"name":"Fred W. Prior","email":"","orcid":"","institution":"University of Arkansas for Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fred","middleName":"W.","lastName":"Prior","suffix":""}],"badges":[],"createdAt":"2025-08-08 19:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7329834/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7329834/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97327264,"identity":"48a12971-199a-47b4-adeb-6578e68c1a20","added_by":"auto","created_at":"2025-12-03 08:46:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":632070,"visible":true,"origin":"","legend":"","description":"","filename":"Aim1FinalSRRevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/57450e93a2296acf77ae40f8.docx"},{"id":97327259,"identity":"d97bab0a-1896-42a6-b01c-546ebe194770","added_by":"auto","created_at":"2025-12-03 08:46:51","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11499,"visible":true,"origin":"","legend":"","description":"","filename":"c9c53104830c4fd7879ebbd2b4537aa9.json","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/c701b1ebfcd6c46fd9ed3fbb.json"},{"id":97327258,"identity":"0015e5fe-8a28-4862-8725-78665f86542f","added_by":"auto","created_at":"2025-12-03 08:46:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23857,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/c0f1de5004bb069f8eb94b84.docx"},{"id":97369727,"identity":"11ac3abd-4de9-4663-83e6-ccf320604c34","added_by":"auto","created_at":"2025-12-03 16:25:37","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17397,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/ed9079489863794309059740.docx"},{"id":97327265,"identity":"9c752c8a-7363-4c2a-b1b4-7355a776ff3f","added_by":"auto","created_at":"2025-12-03 08:46:51","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":129698,"visible":true,"origin":"","legend":"","description":"","filename":"c9c53104830c4fd7879ebbd2b4537aa91enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/fed7c551b604db5f09b18865.xml"},{"id":97370652,"identity":"35ebf68a-98c3-42a7-9857-65a08cfceab2","added_by":"auto","created_at":"2025-12-03 16:27:45","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126439,"visible":true,"origin":"","legend":"","description":"","filename":"c9c53104830c4fd7879ebbd2b4537aa91structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/a785dea5315e9ae1b4a626cb.xml"},{"id":97327266,"identity":"5129e775-22ae-4833-ab84-4e31304b0ab9","added_by":"auto","created_at":"2025-12-03 08:46:51","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141536,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/f285079ba475764a22a9ed79.html"},{"id":97373247,"identity":"90e75623-c69f-4a07-8ac7-dfad289ee5ad","added_by":"auto","created_at":"2025-12-03 16:35:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":783253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/1919bdde-d6e5-43e9-8219-78ace670ab7d.pdf"},{"id":97369871,"identity":"f4572161-721e-4f7e-8c95-38a2359221f6","added_by":"auto","created_at":"2025-12-03 16:25:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23857,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/3d0d2a2765d37a54f56f7e3d.docx"},{"id":97369535,"identity":"582bb3a3-fd12-4dc8-8566-e40ea040148d","added_by":"auto","created_at":"2025-12-03 16:25:06","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17397,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7329834/v1/728a9ae0029369a7a6e9608b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease","fulltext":[{"header":"Background","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) is a complex neurodegenerative disorder that leads to motor deficits, cognitive impairments, and neuropsychiatric symptoms, such as apathy and depression.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] While dysregulation of movement in PD is well characterized, cognitive impairment in PD presents a unique diagnostic challenge, ranging from mild or minor (PD-MCI)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] to major cognitive impairment (PD-Dementia)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] as defined by the Movement Disorder Society. PD-MCI is a distinct intermediate state between PD with normal cognition and PD-Dementia. The presentations of cognitive impairment and disease progression can vary between individuals, but two cognitive subtypes are frequently recognized in the literature: one characterized by frontostriatal and executive dysfunction, often resulting in bradyphrenia, and another involving posterior cortical deficits, marked by visuospatial problems and language/semantic fluency impairments.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Rather than a fixed stage, PD-MCI should be thought of as a spectrum: some people remain at the PD-MCI stage for a long time, some can revert to normal cognition for a period, while others progress to dementia.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Notably, those who revert to normal cognition after a diagnosis of PD-MCI have an elevated risk of declining back to PD-MCI within the next year.[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis cognitive variability is particularly relevant given that PD-MCI is identified in 20\u0026mdash;50% of people at the time of initial PD diagnosis,[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] increasing to 40\u0026mdash;57% after approximately 5 years of disease duration.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] These trends underscore the importance of research focused on understanding the presentations, or patterns, of cognitive impairment in PD. The wide variability in PD-MCI prevalence at diagnoses may, in part, stem from differences in the cognitive domains assessed and psychometric properties of the exams used. This suggests a need for assessments specifically calibrated for PD-MCI. Improving the sensitivity and specificity of these tools could also aid in earlier identification of cognitive impairment in people with PD so therapeutic interventions have a greater chance of potentially slowing disease progression.\u003c/p\u003e\u003cp\u003e The Movement Disorder Society provided guidelines for testing cognitive impairment in PD, including Level II neuropsychological exam batteries\u0026mdash;the gold standard for diagnosis\u0026mdash;and Level I rapid global screening exams (e.g., the Montreal Cognitive Assessment\u0026copy; [MoCA]).[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Currently, when it comes to Level II diagnostic gold standards, clinical practice often faces access barriers, such as the lack of available neuropsychologists, time, cost, or a patient\u0026rsquo;s lack of insurance coverage.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] Therefore, the Level I rapid global cognitive exams are most commonly used to gauge cognitive health.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] The MoCA is the most widely recommended rapid global screening exam, but debates on the accuracy of the exam\u0026rsquo;s scoring method and its specificity and sensitivity for detecting PD-MCI are ongoing.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] According to the MoCA guidelines, the total score is calculated by summing the points from each scorable section, with each cognitive domain weighted based on its assigned point value, and adding an additional point for individuals with \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;12 years of education.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] The summation of the section scores is then compared to a standardized predetermined scale with \u0026ge;\u0026thinsp;26 out of 30 indicating normal cognition. Building on this, discussions with clinicians revealed that, rather than relying solely on the MoCA total score compared to a cutoff as the determinant of cognitive health, they instead interpret patterns in a patient\u0026rsquo;s performance in the context of health-related data. These health-related contextual dependencies are of particular interest, as understanding the patterns that clinicians identify as representative of PD-MCI could lay the groundwork for developing a new method of interpreting the MoCA that improves the sensitivity and specificity for people with PD.\u003c/p\u003e\u003cp\u003eUsing semi-structured interviews and the \u0026ldquo;think aloud protocol\u0026rdquo; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], we investigated the clinical applications of the MoCA. We sought to extract, concurrently, the clinically meaningful features of patient performance on the MoCA exam and health-related data that clinicians consider vital in determining the presence of cognitive impairment in people with PD. By distilling clinically meaningful features, we aimed to synthesize diagnostically significant patterns across the cognitive impairment spectrum in PD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis study employed a descriptive qualitative methodology to explore the perspectives of a specific population.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Guided by the pragmatist\u0026rsquo;s paradigm, which emphasizes real-world observation through the context of a relevant but subjective source, we used purposive sampling to focus on insights from clinical neurologists and neuropsychologists specializing in movement disorders in adult and geriatric populations.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] The Standards for Reporting Qualitative Research checklist was used to ensure transparent reporting of this study (\u003cb\u003eSupplemental 1\u003c/b\u003e).[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] This research approved by the University of Arkansas for Medical Science\u0026rsquo;s Institutional Review Board (IRB Number 274288). All procedures involving human participants followed ethical guidelines in accordance with the Declaration of Helsinki and its later amendments.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eReflexivity of Researchers\u003c/h3\u003e\n\u003cp\u003eThe interdisciplinary research team on this project provided a diverse range of expertise to guide this research and provide valuable insights into real-world clinical practice. Three experienced neurologists provided the foundational concepts and domain-specific expertise critical to the study\u0026rsquo;s conceptualization, and provided continued input throughout the project (TV, RD, HK). An experienced qualitative researcher (MYG) gave valuable guidance on qualitative methodology and trained the lead investigator in interviewing techniques. The lead investigator, (AJE, a biomedical informatics PhD candidate), an expert biomedical informaticist (FWP), and a neurology resident (AT) collaborated to extract the categories and codes to formulate the patterns from the interviews. Including a neurology resident to assist in distilling information from the interviews provided an additional valuable perspective and ensured the informaticists interpreted insights from the interviews accurately and extracted features vital to constructing clinically relevant patterns. An experienced neuroscientist and researcher (LJL-P) lent her extensive expertise in the field of PD research, offering excellent perspectives on cognitive impairment in PD. A researcher in the field of information quality (JT) provided valuable expertise on data quality practices in research to ensure rigorous and transparent data reporting.\u003c/p\u003e\n\u003ch3\u003eDescription of Participants\u003c/h3\u003e\n\u003cp\u003e The Arkansas Clinical Data Repository team at the University of Arkansas for Medical Sciences curated and deidentified completed MoCA exams and neuropsychological reports from the electronic health record system, Epic, in accordance with regulatory requirements.[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] The curation criteria for the retrospective patient data for this study were: (i) Primary diagnosis of PD and a secondary diagnosis of MCI or Dementia from PD in their medical record; (ii) A completed MoCA exam and neuropsychological report in the health record.\u003c/p\u003e\u003cp\u003eDuring the study design, it was determined that a total of nine patients\u0026rsquo; MoCA exams and neuropsychological reports were optimal based on the availability of recruited clinical professionals and the number of assessments that could be reasonably reviewed in a 60-minute interview time constraint. The nine MoCA exams and neuropsychological reports were identified with the assistance from clinical colleagues in the Neurology and Geriatric Departments at the University of Arkansas for Medical Sciences. The assessments were organized into three groups of three sets of exam-report pairs (herein referred to as patient profiles). The neuropsychological reports contained a comprehensive overview of the patient, describing presenting concerns, biopsychosocial history, psychiatric symptoms, activities of daily living, neurobehavioral status, cognitive domain test results, clinical impressions, diagnostic summary, and recommendations from the time of the neuropsychological appointment. Notably, while the health information was current at the neuropsychological appointment, the completed MoCA exam in their file could have been administered prior to or after the time of the neuropsychological appointment date. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides the administration date for both assessments and the final diagnosis by patient ID number. The assessments were completed with a mean difference of 1.70 years (SD \u0026asymp; 1.38 years). Despite this time gap, the report generated from the neuropsychological appointment was a vital assessment that succinctly provided pertinent health information traditionally available if the interviewee were the patient\u0026rsquo;s clinical specialist, thereby simulating real-world clinical practice. In our retrospective data set, three patients were diagnosed with PD with mild cognitive impairment non-amnestic (PD-MCI non-A), two were categorized as amnestic mild cognitive impairment (PD-MCI A), and two were classified as PD-Dementia (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe Recorded Date of Administration for the Assessments and Final Diagnosis by Patient ID Number\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoCA Date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeuropsychological Report Date\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFinal Diagnosis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4/2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4/2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDementia due to PD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4/2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild cognitive impairment (non-amnestic)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10/2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6/2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAmnestic mild cognitive impairment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11/2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAmnestic mild cognitive impairment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11/2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8/2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild cognitive impairment (non-amnestic)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4/2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11/2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDementia due to PD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11/2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7/2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild cognitive impairment (non-amnestic)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10/2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMild cognitive impairment (non-amnestic)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6/2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12/2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDementia due to PD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAll potential interviewees were provided a brief overview of the research purpose and participant protections at the time of recruitment to review upon deciding to participate, thus acknowledging they understood the benefits and risks of participation. A total of six clinical professionals were recruited, clinical neurologists (n\u0026thinsp;=\u0026thinsp;5) and a neuropsychologist (n\u0026thinsp;=\u0026thinsp;1), from two different hospital systems to participate in the interviews and ensure we gathered, at minimum, two opinions on each patient profile. The recruitment criteria required potential participants to be currently practicing clinical neurologists or neuropsychologists specializing in movement disorders in the adult to geriatric population. The advising neurologist (RD), the senior author and biomedical informatics expert (FWP), and the lead investigator (AJE) recruited potential interviewees by corresponding with colleagues, associates, and mentors.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003e Verbal informed consent was obtained from all volunteer interviewees prior to beginning the interview recording. The interviewees received their three assigned patient profiles one week in advance and were asked to review them prior to the interview. The semi-structured interviews were formatted using the think aloud protocol,[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] an interview technique designed to study thought processes, which has previously been leveraged to gain insights into medical decision-making for diagnostic techniques and investigations into the clinical understanding of cognitive impairment.[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] The think aloud protocol requires the interviewee to speak their thoughts aloud as they perform a specific task, thereby providing a verbal account of their critical thinking and decision-making processes.\u003c/p\u003e\u003cp\u003eThe interviewer (AJE) utilized an interview guide developed by research team members (AJE, FWP, RD, MYG) (\u003cb\u003eSupplemental 2\u003c/b\u003e). The interview questions were developed using an iterative approach and pilot-tested with an advising clinician (RD) to determine applicability. The purpose of the interview guide was to assist the interviewer in encouraging the interviewees to articulate their observations as they reviewed their three assigned patient profiles. The questions focused on the patterns of patient performance on the MoCA exam, what important health features an interviewee would want to know from the neuropsychological report, and how they interpreted any context dependence between the two assessments and if they agreed with the diagnosis of the patient. For example, the question, \u0026ldquo;What do you observe/look for in the patient\u0026rsquo;s performance?\u0026rdquo; was used in the MoCA section to prompt detailed descriptions of clinicians\u0026rsquo; observation and interpretive practices when reviewing the exam. A similar question was used in the neuropsychology report portion of the guide: \u0026ldquo;What do you look for in this section? What does its presence/absence mean to you?\u0026rdquo; To continually prompt the interviewee to verbalize their thought processes as they reviewed the documents this question was used: \u0026ldquo;Could you talk me through the aspects of the narrative that helped you determine that?\u0026rdquo; Additionally, the guide functioned as a reference for the interviewer to keep the discussion focused and within the allotted time frame. To further simulate real-time clinical practice procedures, the interviewees were instructed to share their computer screens during the interview, allowing them to review the patient profiles freely according to their workflow methodology.\u003c/p\u003e\u003cp\u003eA trained qualitative researcher (AJE) conducted interviews via videoconference lasting\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;60 minutes. The interviews were recorded and stored on a HIPAA-compliant platform. All interviews were transcribed verbatim by AJE. To ensure interviewee privacy protections, personal identifiers in the transcripts were deidentified using an alphanumeric code prior to sharing with colleagues contributing to the interview coding process.\u003c/p\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eRecurrent themes in the data were identified by inductive and deductive hybrid thematic analysis.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] The adaptive approach allowed inductive elements to emerge by identifying key features essential to the differential diagnostic process. Simultaneously, deductive reasoning ensured a focus on the research question: whether the features noted as important for determining cognitive health by the interviewees formed diagnostically meaningful patterns of cognitive impairment in PD. As this study was largely explorative, features of importance were extracted by recursive open coding of the transcripts,[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] which allowed for refinement and triangulation as the interviews progressed to enhance the rigor of the deductive and inductive content analysis and subsequent extraction of patterns.[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays an example of the methods used to derive important features underlying the codes and categories from interview quotes that reflecting clinicians\u0026rsquo; observations. The table shows the document the interviewee was discussing while making an observation about patient performance on the MoCA, relationships between MoCA performance and health-related data from the neuropsychological report, and other clinically relevant details from the report that may impact MoCA performance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExample Quotes with Associated Codes and Categories from Qualitative Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuote\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCode\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDocument\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eImportant Feature(s)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;I mean we always have their med list and medical history and things like that\u0026hellip;\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUse of medical background\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eContextual health data for cognitive assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuropsychological report\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMedication list, medical history\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;So gives me a little bit of insight, you know, years of education, what they did for a living, you know who their family is and who their support system is and that sort of thing. So, so I find those things to be\u0026hellip;helpful to kind of put everything into context.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eContextual patient background\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBiopsychosocial contextualization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNeuropsychological report\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYears of education, occupational history, family support system\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e\u0026ldquo;\u0026hellip;when I moved down to the kind of registration part of the memory looks like it took. A few tries to get it correct, looks like they may have had some difficulty with hearing when they said faith instead of face.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInterpreting test behavior through sensory/context cues\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExam performance contextualization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMoCA exam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNumber of tries to get MoCA memory task correct, hearing loss\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe data was input into Microsoft Excel, using a row per patient profile (n\u0026thinsp;=\u0026thinsp;9), grouping features of importance from the assigned interviewees to establish one pattern per patient profile. Features were deemed important based on collaborative discussions between AJE and FWP, as they reviewed the interview transcripts creating codes and categories according to their interpretations of what the interviewee pointed out as diagnostically meaningful. A neurology resident (AT) was recruited to review the interview transcripts independently, extract features of importance and organize these features into patterns for each patient profile as AJE and FWP had previously done. Meetings between the three interview coders (AJE, FWP, AT) were held to reach a consensus on each patient profile\u0026rsquo;s important features and respective pattern. Then, all three interview coders independently reviewed each pattern to determine if any could be combined using these devised pattern reduction rules: (i) are there clinically meaningful differences and, (ii) can the features be quantified? Clinical meaningfulness was based on the frequency a specific feature was mentioned in an interview and its co-occurrence with other features. Only features that could be quantitively represented\u0026mdash;either using a binary indicator for presence (1) or absence (0), or numerically (e.g., structural similarity index of the cube example and patient drawing from the MoCA)\u0026mdash;were retained. A consensus meeting was held to finalize the list of patterns.\u003c/p\u003e\u003cp\u003eTo statistically compare patient profile demographics and cognitive exam scores between the three independent groups (PD-MCI non-A, PD-MCI A, PD-Dementia) the Kruskal-Wallis test was used. This test is preferred for small sample sizes\u0026mdash;requiring only two people per group\u0026mdash;and it does not assume normality or equal variance, which is appropriate given our unequal and small group sizes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the patient demographics and the Kruskal-Wallis statistical test effect sizes for the patient\u0026rsquo;s age, number of years of education, total MoCA score and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] total index score. The nine patient profiles were divided into three groups according to the diagnostic impression in the neuropsychological report (PD-MCI non-A, PD-MCI A, PD-Dementia). The Kruskal-Wallis Test revealed a significant difference in RBANS scores across diagnostic groups (H (2)\u0026thinsp;=\u0026thinsp;6.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.047, η\u0026sup2; = 0.76). Post-hoc Dunn\u0026rsquo;s test with Bonferroni correction showed that the PD-MCI non-A group scored significantly higher than the PD-Dementia group (adjusted \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.043), which is expected as a higher score suggests better cognitive functioning.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient Profile Demographics, Years of Education, MoCA and RBANS Total Scores\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroups Compared\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eη\u0026sup2; (Effect Size)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003ePD-MCI non-A, PD-MCI A, PD-Dementia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSmall\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears of Edu.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRBANS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003csup\u003e\u003cb\u003eⱡ\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: \u003cem\u003eInterpretation thresholds for η\u0026sup2;: Small\u0026thinsp;\u0026asymp;\u0026thinsp;0.01, Medium\u0026thinsp;\u0026asymp;\u0026thinsp;0.06, Large\u0026thinsp;\u0026ge;\u0026thinsp;0.14; ⱡ p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/em\u003e\u003c/p\u003e\u003cp\u003eDuring an interview, it was discovered that the performance of patient ID 6 on the MoCA exam and cognitive assessment battery in the neuropsychological report was most consistent with a diagnosis of delirium rather than impairment due to PD. This resulted in removal of this patient profile, as it could not be definitively\u003c/p\u003e\u003cp\u003edetermined that the patient\u0026rsquo;s performance on the MoCA exam was unaffected by\u003c/p\u003e\u003cp\u003edelirium, thereby reducing the number of patient profiles to eight (n\u0026thinsp;=\u0026thinsp;8). Patient ID 1 was added to the affected group of three patient profiles to supplement the deficit, resulting in three clinical opinions for this patient profile. Additionally, one of the two clinicians that reviewed Patient ID 8 expressed that they disagreed with the diagnosis of PD-MCI non-A recorded in the neuropsychological assessment report. Instead, they suggested that the patient\u0026rsquo;s performance on the MoCA and extent of affected activities of daily living recorded in the neuropsychological report reflected mild PD-Dementia.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates how codes and categories were derived to capture specific features that clinicians considered necessary for their differential diagnostic processes as they reviewed the patient profiles. The \u0026lsquo;Important Feature(s)\u0026rsquo; column presents the synthesized features from interview quotes, reflecting how clinicians integrated medical history (e.g., medication lists), contextual health features that influence MoCA performance (e.g., hearing issues), and broader personal information (e.g., presence of family support) to interpret the patient\u0026rsquo;s cognitive functioning beyond the MoCA total score. An example of how we used qualitative methods to synthesize the important features composing the patterns represented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e can be seen by directing your attention to the third quote in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This quote shows how a clinical professional utilized contextual patient health information to interpret performance on the MoCA. The quote is extracted from an interview where the clinician was reviewing the Memory subtask of the MoCA exam and noted that when the patient was supposed to repeat the word \u0026lsquo;face\u0026rsquo; instead presumably said \u0026lsquo;faith\u0026rsquo;, per a note on the exam document. This note prompted the interviewee to consider if the patient had a hearing impairment. This important feature is reflected in Pattern 3 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e in the \u0026lsquo;MoCA Section\u0026rsquo; column noted as \u0026lsquo;Memory: number of tries, correlate to hearing issue\u0026rsquo;.\u003c/p\u003e\u003cp\u003eSeveral features were repeatedly identified by the majority of interviewees as clinically relevant to interpreting a patient\u0026rsquo;s performance on the MoCA or important contextual health information. Consequently, these features appeared in all three final patterns and are recorded in the \u0026lsquo;Recurrent Features\u0026rsquo; row of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The repetition reflects consistent clinical relevance based on the patter reduction criteria described above, demonstrating that pattern features are not mutually exclusive.\u003c/p\u003e\u003cp\u003eIn the \u0026lsquo;MoCA Section\u0026rsquo; column of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, there are several correlations mentioned between a specific cognitive domain section and health-related data from the neuropsychological report. The Language and Attention feature in Pattern 1 of the \u0026lsquo;MoCA Section\u0026rsquo; column, includes checking performance for lexical fluency. Lexical capabilities are often diminished when cognitive functions are degraded by Alzheimer\u0026rsquo;s disease pathology, cardiovascular issues,[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] or vascular risk factors,[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] making these critical contextual health features to check for and are noted within the \u0026lsquo;Health Data\u0026rsquo; column. In Pattern 2, a feature in the Attention and Language domain sections in the \u0026lsquo;MoCA Section\u0026rsquo; column notes that task performance must be normalized by education. This feature points to the importance of contextual biopsychosocial factors, as individuals with higher educational degrees tend to perform better on these tasks.[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] In Pattern 3, the Memory feature listed in the \u0026lsquo;MoCA Section\u0026rsquo; column illustrates how a clinician might suspect that a physical health-related feature is impacting exam performance. To complete the Memory subsection of the MoCA exam, patients must listen to words said by the exam administrator and repeat them back; however, if a patient has hearing aids or a hearing impairment and performs poorly, the results may reflect a physical limitation rather than a cognitive dysfunction.\u003c/p\u003e\u003cp\u003eSimilarly, in the Clock task of the \u0026lsquo;MoCA Section\u0026rsquo; within Patterns 2 and 3, one feature notes the small size of the analog clock numbers, referred to in the literature as \u0026lsquo;micrographia\u0026rsquo;, a sequela of PD commonly linked to motor dysfunction.[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] While this may appear irrelevant for understanding presentations of the cognitive impairment spectrum in PD, patterns are produced by the extraction of features of importance that clinical professionals recognized while interpreting the assessments. The patterns in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e offer a holistic perspective of patient health derived from the eight model patient profiles used to create them. Therefore, we did not eliminate features involving motor symptoms as they were determined to be clinically meaningful because they were verbalized by the clinical professionals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinically Meaningful Features from the MoCA exam and Neuropsychological Report\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoCA Exam Section\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeuropsychological Report\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrent features in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eall\u003c/span\u003e patterns:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrail Task\u003c/b\u003e: Evidence of errors (e.g., \u0026ldquo;Any errors\u0026rdquo;)\u003c/p\u003e\u003cp\u003e\u003cb\u003eCube\u003c/b\u003e: Difference from ideal cube (e.g., 3D-ness)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e- Medication list\u003c/p\u003e\u003cp\u003e- Sleep disorder/issues\u003c/p\u003e\u003cp\u003e- Impaired independence of activities of daily living\u003c/p\u003e\u003cp\u003e- Years of education\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern 1:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrail Task\u003c/b\u003e: Number of errors\u003c/p\u003e\u003cp\u003e\u003cb\u003eCube\u003c/b\u003e: Jitter in lines, degree of skew\u003c/p\u003e\u003cp\u003e\u003cb\u003eClock\u003c/b\u003e: Numbers not sequential or missing, no short/long hand, position of hands, contour\u003c/p\u003e\u003cp\u003e\u003cb\u003eLanguage and Attention\u003c/b\u003e: Lexical fluency and serial 7 (\u003cb\u003eAttention\u003c/b\u003e) tasks- check lexical fluency for Alzheimer\u0026rsquo;s disease, the total number of words given in lexical fluency task\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e- Hearing loss/aids\u003c/p\u003e\u003cp\u003e- Vision loss\u003c/p\u003e\u003cp\u003e- B12 deficiency\u003c/p\u003e\u003cp\u003e- Evidence of delirium\u003c/p\u003e\u003cp\u003e- Vascular risk factors\u003c/p\u003e\u003cp\u003e- Activity level\u003c/p\u003e\u003cp\u003e- Premorbid level of functioning\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern 2:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrail Task\u003c/b\u003e: Correction of errors (extra lines)\u003c/p\u003e\u003cp\u003e\u003cb\u003eCube\u003c/b\u003e: Jitter in lines\u003c/p\u003e\u003cp\u003e\u003cb\u003eClock\u003c/b\u003e: Small size of numbers, evidence of correction of error (extra lines)\u003c/p\u003e \u003cp\u003e\u003cb\u003eAttention\u003c/b\u003e: Serial 7s and letter vigilance task performance (normalized by years of education), Attention test: note early stopping on letter vigilance task, delayed recall section, or lexical fluency task (\u003cb\u003eLanguage\u003c/b\u003e) \u003cb\u003eLanguage\u003c/b\u003e: Number of words on lexical fluency task normalized by years of education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e- Medication specifics (cognition-impacting drugs)\u003c/p\u003e\u003cp\u003e- Occupational exposure to pesticides\u003c/p\u003e\u003cp\u003e- Evidence of urinary tract infection\u003c/p\u003e\u003cp\u003e- Family history of dementia\u003c/p\u003e\u003cp\u003e- History of concussion(s)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern 3:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTrail Task\u003c/b\u003e: Level of completion, jitter of lines\u003c/p\u003e\u003cp\u003e\u003cb\u003eCube\u003c/b\u003e: size, shape, orientation\u003c/p\u003e\u003cp\u003e\u003cb\u003eClock\u003c/b\u003e: Number of tries, percent complete, size of circle and numbers, contour, orientation, number spacing/order, position and length of clock hands\u003c/p\u003e\u003cp\u003e\u003cb\u003eMemory\u003c/b\u003e: Number of tries, correlate to hearing issue\u003c/p\u003e\u003cp\u003e\u003cb\u003eLanguage and Attention\u003c/b\u003e: Lexical fluency and serial 7 task (\u003cb\u003eAttention\u003c/b\u003e) (normed by years of education) correlate with attention issues \u003cb\u003eDelayed Recall\u003c/b\u003e: Categorical vs. multiple choice cue success\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e- Medication specifics (PD medication timing, sedatives, dementia drugs)\u003c/p\u003e\u003cp\u003e- Vision loss\u003c/p\u003e\u003cp\u003e- Hearing loss/aids\u003c/p\u003e\u003cp\u003e- Arthritis\u003c/p\u003e\u003cp\u003e- Family support\u003c/p\u003e\u003cp\u003e- Vascular risk factors\u003c/p\u003e\u003cp\u003e- Cardiovascular disease indicators\u003c/p\u003e\u003cp\u003e- Premorbid level of functioning\u003c/p\u003e\u003cp\u003e- Social engagement\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur objective was to distill diagnostically meaningful patterns of cognitive impairment in people with PD by examining how clinicians interpreted the MoCA in conjunction with patient health data. The interviews with clinical experts revealed the discrepancy between the currently recommended MoCA scoring method and its clinical application. Each section of the MoCA exam includes a checkbox for task completion and the exam instructions advise the proctor to award or deduct points based on specific criteria. For example, giving a point if the patient drew relatively parallel lines for reproducing a 3D cube in the Cube sub-section, or deduct a point for an incorrect word recall in the Delayed Recall section. This research supported the insights shared by clinical mentors during the initial conceptualization of the study, that clinicians move beyond adherence to the MoCA scoring instructions. Instead, relying on pattern recognition\u0026mdash;an integral part of differential diagnostics\u0026mdash;to interpret patterns of patient performance on the MoCA exam in relation to health features. Our results, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate these important features that clinicians see as diagnostically vital, such as noting the presence of arthritis\u0026mdash;noted in Pattern 3\u0026mdash;which could impact their drawing of a 3D cube, or if the patient is taking medications that could impair memory capacity\u0026mdash;noted in Pattern 2.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] This is particularly notable given that medications used to treat the motor and non-motor symptoms in PD can cause side effects like sleep disturbances, drowsiness, or memory problems.[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] Additionally, many people with PD experience anxiety, depression, and apathy, which can affect their performance on global screening exams like the MoCA, further highlighting the importance of integrating contextual health information into exam scoring procedures.[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] These relationships are typically considered implicitly by clinicians during exam interpretation; however, our analysis makes this diagnostic reasoning explicit.\u003c/p\u003e\u003cp\u003eOther rapid screening tools with comparable administration time to the MoCA exam such as the Parkinson\u0026rsquo;s Disease-Cognitive Rating Scale,[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] the Scales for Outcomes of Parkinson\u0026rsquo;s Disease,[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] or the Parkinson Neuropsychometric Dementia Assessment[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] are specifically designed for detecting cognitive deficits in people with PD but are not commonly employed in the clinical setting. The assimilation of new tools in the clinic is often lengthy and challenging, as evidenced by the high failure rate of clinical decision support systems.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] This difficulty is primarily due to the disruptive impact on clinical workflows, compounded by the need for training and certifications necessary for successful implementation. Following this evidence, a similar challenge would likely arise with the introduction of a new global screening tool, which may help explain the reason why PD-specific screening exams are not as widely used as the MoCA. This reasoning prompted the selection of the MoCA exam as the model global screening tool for this study. Additionally, we believe that optimizing an established and popular global screening exam would provide the foundation for a more rapid acceptance of any future recommended scoring alterations.\u003c/p\u003e\u003cp\u003eOur research has several limitations that must be acknowledged. The first is the small sample size of both interviewees (n\u0026thinsp;=\u0026thinsp;6) and the number of patient profiles (n\u0026thinsp;=\u0026thinsp;8). The small sample size of interviewees was a result of limited access to clinical neurologists and neuropsychologists in the home institution the study was conducted. Five interviewees were recruited from the study\u0026rsquo;s home institution, and one interviewee was recruited from an external healthcare institution. Our cohort had five clinical neurologists, who are considered experts in their respective fields of neurology and one neuropsychologist who sees many patients with movement disorders. Recruitment of only one neuropsychologist was due to limited availability of these specialized professionals. Particularly those who frequently work with older adults with movement disorders. Additionally, it must be mentioned that this study\u0026rsquo;s premise is to explore patterns of patient performance on the MoCA in relation to their health data. Neuropsychologists are experts in curating cognitive batteries specifically tailored for analysis of discrete aspects of a patient\u0026rsquo;s cognition and do not traditionally utilize the MoCA exam as a component of their battery. For this reason, while prudent to include a neuropsychologist\u0026rsquo;s opinion given their insights into the neuropsychological effects of a neurodegenerative disorder such as PD, it was not necessary to recruit additional participants. Furthermore, inductive thematic saturation was achieved during analysis of the interview transcripts as we used purposive sampling to gather a homogenous expert population of clinical professionals specializing in movement disorders and geriatrics. The small sample size of patient profiles (n\u0026thinsp;=\u0026thinsp;9) was based on maximizing the number of patient assessments that could be reasonably reviewed within the 60-minute interview time limit leading to the to the conclusion that reviewing three patient profiles per interview would be practical.\u003c/p\u003e\u003cp\u003eAnother limitation is that the final number of patterns (n\u0026thinsp;=\u0026thinsp;3) is small. However, these patterns emerged through thematic saturation based on the recurrence of specific features across all interviews and the frequency with which certain features appeared together. Arguably, a larger number of patterns could be extracted by decomposing these three patterns, but focusing on a smaller set of distilled patterns offers a more targeted approach to identifying whether these patterns can be meaningfully detected in a larger dataset. This focused approach enhances our ability to explore how clinical professionals interpret patient performance on the MoCA and health-related data when mining for differential diagnostic insights. We also would like to acknowledge that one of the primary purposes of the MoCA is to track longitudinal changes in cognition. The exam\u0026rsquo;s inclusion of a broad range of cognitive domains, especially visuospatial executive functions which are commonly affected by PD progression, makes it a valuable screening tool. However, for the purposes of this study, a single time point was sufficient to capture the important features clinicians note during their differential diagnostic processes.\u003c/p\u003e\u003cp\u003eAn additional limitation was the time frame between when the MoCA exam was completed, and when the neuropsychological report was created. It could be reasonably argued that the time gap affected correlations between the patient\u0026rsquo;s performance on the MoCA exam and their health data recorded in the report. The majority of the MoCA exams and neuropsychological reports were completed with a mean of 1.70 years (SD \u0026asymp; 1.38 years) of each other, apart from Patient ID 4, who had 5 years and 2 months between (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, it should be noted that we were extracting features that are clinically relevant for \u003cem\u003eassessing\u003c/em\u003e the performance on the MoCA exam by people with PD on the cognitive impairment spectrum\u0026mdash;as noted in the \u0026lsquo;Final Diagnosis\u0026rsquo; column of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026mdash;rather than \u003cem\u003edetermining\u003c/em\u003e the presence of cognitive impairment. The purpose of presenting the three different patient profiles with a wide range of PD symptomology to each interviewee was to ensure we prompted multiple different contexts and elicited complex pattern recognition skills.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatterns in health data have previously been leveraged to differentiate the diverse presentations of PD. Imaging studies have successfully distinguished idiopathic PD from other similarly presenting degenerative disorders, such as progressive supranuclear palsy or related parkinsonisms.[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] Other research has also described the utility of a patient\u0026rsquo;s health history and physical exams in identifying clinical patterns of disease-causing pulmonary dysfunction.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] Through this lens, we can extrapolate that when patient health-related data is considered, there are discriminating disease features to be gleaned. Unfortunately, reliable and robust detection of cognitive deficits in PD remains a persistent challenge. This is particularly concerning given that PD-MCI reduces patient quality of life, is a risk factor for developing dementia, and increases the risk of early mortality.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] In the absence of preventative treatments for cognitive impairment in PD, and with the estimated prevalence of PD increasing globally, the need to develop robust screening exams for early detection is urgent.[\u003cspan additionalcitationids=\"CR50 CR51 CR52\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eWe argue that by leveraging the innate human capacity for pattern recognition,[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] which is the grounded authority of the differential diagnostic process,[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] we can begin to decipher clinically relevant disease patterns within the complex symptomology of the cognitive impairment spectrum in PD. This insight could optimize cognitive exams for PD, thereby facilitating earlier identification and more timely introduction of interventions, such as exercise, to slow disease progression.[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] Extracting diagnostically meaningful patterns of cognitive impairment was the first step toward achieving that goal. The next is to quantitatively represent the extracted pattern features and determine if they exist outside of this sample data set in a large corpus of MoCA exams and patient health data.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHIPPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHealth Insurance Portability and Accountability Act\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eParkinson\u0026rsquo;s Disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDementia\u0026mdash;Parkinson\u0026rsquo;s Disease with Major Cognitive Impairment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMCI\u0026mdash;Parkinson\u0026rsquo;s Disease with Mild (Minor) Cognitive Impairment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMCI A\u0026mdash;Parkinson\u0026rsquo;s Disease with Mild Cognitive Impairment Amnestic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMCI non\u0026mdash;A\u0026mdash;Parkinson\u0026rsquo;s Disease with Mild (Minor) Cognitive Impairment Non\u0026mdash;Amnestic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRBANS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRepeatable Battery for the Assessment of Neuropsychological Status\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard Deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate:\u003c/em\u003e The University of Arkansas for Medical Science\u0026rsquo;s Institutional Review Board approved the study (IRB Number 274288). All human participants provided verbal informed consent prior to the start of the interview recording. All research procedures were conducted in accordance with the ethical standards of the home intuition of the study and with the Declaration of Helsinki and its later amendments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication:\u003c/em\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials:\u003c/em\u003e The datasets generated and/or analyzed during the current study are available in the Zenodo repository, the interview transcripts (DOI: 10.5281/zenodo.16861070), and the Excel document describing how the patterns were derived from the interviews (DOI: 10.5281/zenodo.16859473).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests:\u003c/em\u003e The authors declare that they have no competing interests with respect to the research, authorship, and/or publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003eThis material is based upon work supported by the National Science Foundation under Award No. OIA-1946391. Additionally, the research reported here is partially supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UM1 TR004909. The content of this original research is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation or the National Institutes of Health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; Contributions:\u003c/em\u003e AJE and FWP led the study design with foundational conceptual support from TV, RD, and HK. MYG trained AJE in qualitative research methodology and reviewed the study protocol for accuracy. AJE conducted all interviews and authored transcripts. AT, FWP, and AJE extracted features from transcripts to create patterns. FWP, LJL-P, JT, MYG, TV, RD, and HK reviewed and critiqued the manuscript. All authors were responsible for the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments:\u003c/em\u003eWe thank all the interviewees who volunteered for our study, without whom this research would not have been possible. Likewise, we would like to thank M. Denise Compton, PhD, who donated her time to collaborate with our clinical colleagues to identify the patient profiles. We also thank the Arkansas Clinical Data Repository team, especially Kim D. Gates, BBA, who led the curation and deidentification efforts. The authors would also like to thank MoCA Test Inc.\u0026copy; (All rights reserved) for allowing us to use the MoCA exam for this academic research. We also acknowledge the support provided during preparation of this manuscript made possible by the Scholarly Writing Program of the Center for Childhood Obesity Prevention funded by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number P20GM109096 (Arkansas Children\u0026rsquo;s Research Institute, PI: Borsheim).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBaiano C, Barone P, Trojano L, Santangelo G. Prevalence and clinical aspects of mild cognitive impairment in Parkinson\u0026rsquo;s disease: A meta-analysis. Movement Disorders. 2020;35:45\u0026ndash;54.\u003c/li\u003e\n\u003cli\u003eWeintraub D, Caspell-Garcia C, Simuni T, Cho HR, Coffey CS, Aarsland D, et al. Neuropsychiatric symptoms and cognitive abilities over the initial quinquennium of Parkinson disease. Ann Clin Transl Neurol. 2020;7:449\u0026ndash;61.\u003c/li\u003e\n\u003cli\u003eLitvan I, Goldman JG, Tr\u0026ouml;ster AI, Schmand BA, Weintraub D, Petersen RC, et al. Diagnostic criteria for mild cognitive impairment in Parkinson\u0026rsquo;s disease: Movement Disorder Society Task Force guidelines. Movement Disorders. 2012;27:349\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eKulisevsky J, Litvan I, Weintraub D, Goldman JG, Tr\u0026ouml;ster AI, Lewis SJG, et al. A Call for Change: Updating the Operational Definition for Dementia in Parkinson\u0026rsquo;s Disease. Mov Disord Clin Pract. 2025;12:296\u0026ndash;301.\u003c/li\u003e\n\u003cli\u003eGoldman JG, Vernaleo BA, Camicioli R, Dahodwala N, Dobkin RD, Ellis T, et al. Cognitive impairment in Parkinson\u0026rsquo;s disease: a report from a multidisciplinary symposium on unmet needs and future directions to maintain cognitive health. NPJ Parkinsons Dis. Nature Publishing Group; 2018.\u003c/li\u003e\n\u003cli\u003eAarsland D, Batzu L, Halliday GM, Geurtsen GJ, Ballard C, Ray Chaudhuri K, et al. Parkinson disease-associated cognitive impairment. Nat Rev Dis Primers. 2021;7.\u003c/li\u003e\n\u003cli\u003eJones JD, Kuhn TP, Szymkowicz SM. Reverters from PD-MCI to cognitively intact are at risk for future cognitive impairment: Analysis of the PPMI cohort. Parkinsonism Relat Disord. 2018;47:3\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eSantangelo G, Vitale C, Picillo M, Moccia M, Cuoco S, Longo K, et al. Mild Cognitive Impairment in newly diagnosed Parkinson\u0026rsquo;s disease: A longitudinal prospective study. Parkinsonism Relat Disord. 2015;21:1219\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eSaredakis D, Collins-Praino LE, Gutteridge DS, Stephan BCM, Keage HAD. Conversion to MCI and dementia in Parkinson\u0026rsquo;s disease: a systematic review and meta-analysis. Parkinsonism Relat Disord. Elsevier Ltd; 2019. p. 20\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eCastelli MB, Alonso-Recio L, Carvajal F, Serrano JM. Does the Montreal Cognitive Assessment (MoCA) identify cognitive impairment profiles in Parkinson\u0026rsquo;s disease? An exploratory study. Applied Neuropsychology:Adult. 2024;31:238\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eBroeders M, De Bie RMA, Velseboer DC, Speelman JD, Muslimovic D, Schmand B. Evolution of mild cognitive impairment in Parkinson disease [Internet]. 2013. Available from: www.neurology.org\u003c/li\u003e\n\u003cli\u003ePostuma RB, Berg D, Stern M, Poewe W, Olanow CW, Oertel W, et al. MDS clinical diagnostic criteria for Parkinson\u0026rsquo;s disease. Movement Disorders. John Wiley and Sons Inc; 2015. p. 1591\u0026ndash;601.\u003c/li\u003e\n\u003cli\u003eLitvan I, Aarsland D, Adler CH, Goldman JG, Kulisevsky J, Mollenhauer B, et al. MDS task force on mild cognitive impairment in Parkinson\u0026rsquo;s disease: Critical review of PD-MCI. Movement Disorders. 2011;26:1814\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eNasreddine ZS, Phillips NA, B\u0026eacute;dirian V, Charbonneau S, Whitehead V, Collin I, et al. The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53:695\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eAuclair-Ouellet N, Mandl S, Kibreab M, Haffenden A, Hanganu A, Cheetham J, et al. Characterization of cognition in mild cognitive impairment with and without Parkinson\u0026rsquo;s disease. Clin Park Relat Disord. 2020;3.\u003c/li\u003e\n\u003cli\u003eRosenblum S, Meyer S, Gemerman N, Mentzer L, Richardson A, Israeli-Korn S, et al. The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson\u0026rsquo;s Disease? Mov Disord Clin Pract. 2020;7:648\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eDalrymple-Alford J, MacAskill M, Nakas C, Livingston L, Graham BC, Crucian MG, et al. The MoCA Well-suited screen for cognitive impairment in Parkinson disease [Internet]. Neurology \u0026reg;. 2010 p. 1717\u0026ndash;25. Available from: www.mocatest.org\u003c/li\u003e\n\u003cli\u003eRosenblum S, Meyer S, Gemerman N, Mentzer L, Richardson A, Israeli-Korn S, et al. The Montreal Cognitive Assessment: Is It Suitable for Identifying Mild Cognitive Impairment in Parkinson\u0026rsquo;s Disease? Mov Disord Clin Pract. 2020;7:648\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eFengler S, Kessler J, Timmermann L, Zapf A, Elben S, Wojtecki L, et al. Screening for cognitive impairment in Parkinson\u0026rsquo;s disease: Improving the diagnostic utility of the MoCA through subtest weighting. PLoS One. 2016;11.\u003c/li\u003e\n\u003cli\u003eNielsen J, Clemmensen T, Yssing C. Getting access to what goes on in people\u0026rsquo;s heads?-Reflections on the think-aloud technique. Proceedings of the second Nordic conference on Human-computer interaction. 2002;101\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eJ\u0026auml;\u0026auml;skel\u0026auml;inen R. Think-aloud protocol. In: Gambier Y, Doorslaer van L, editors. Handbook of translation studies. John Benjamins publishing company; 2012. p. 371\u0026ndash;3.\u003c/li\u003e\n\u003cli\u003eBradshaw C, Atkinson S, Doody O. Employing a Qualitative Description Approach in Health Care Research. Glob Qual Nurs Res. 2017;4.\u003c/li\u003e\n\u003cli\u003eAllemang B, Sitter K, Dimitropoulos G. Pragmatism as a paradigm for patient-oriented research. Health Expectations. John Wiley and Sons Inc; 2022. p. 38\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Brien BC, Harris IB, Beckman TJ, Reed DA, Cook DA. Standards for reporting qualitative research: A synthesis of recommendations. Academic Medicine. 2014;89:1245\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eBaghal A, Zozus M, Baghal A, Al-Shukri S, Prior F. Factors associated with increased adoption of a research data warehouse. Stud Health Technol Inform. 2019;257:31\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eBlair EM, Zahuranec DB, Forman J, Reale BK, Langa KM, Giordani B, et al. Physician Diagnosis and Knowledge of Mild Cognitive Impairment. Journal of Alzheimer\u0026rsquo;s Disease. 2022;85:273\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eHarold C. Sox, Michael C. Higgins, Douglas K. Owens. Differential Diagnosis. Medical Decision Making. Second. Chichester, UK: John Wiley \u0026amp; Sons, Ltd; 2013. p. 7\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eWolcott MD, Lobczowski NG. Using cognitive interviews and think-aloud protocols to understand thought processes. Curr Pharm Teach Learn. 2021;13:181\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eFereday J, Adelaide N, Australia S, Eimear Muir-Cochrane A. Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development. 2006.\u003c/li\u003e\n\u003cli\u003eSchell D. Pattern Recognition as an Indicator of Diagnostic Expertise. Algarve; 2013 Feb.\u003c/li\u003e\n\u003cli\u003eBabchuk WA. Fundamentals of qualitative analysis in family medicine. Fam Med Community Health. 2019;7:e000040.\u003c/li\u003e\n\u003cli\u003eSeers K. Qualitative data analysis. Evid Based Nurs. 2012. p. 2.\u003c/li\u003e\n\u003cli\u003eRandolph C, Tierney MC, Mohr E, Chase TN. Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [Internet]. 1998. Available from: http://www.harcourt-uk.com/product.\u003c/li\u003e\n\u003cli\u003eGrodstein F. Cardiovascular risk factors and cognitive function. Alzheimer\u0026rsquo;s and Dementia. 2007;3. \u003c/li\u003e\n\u003cli\u003eCamerino I, Ferreira J, Vonk JM, Kessels RPC, de Leeuw FE, Roelofs A, et al. Systematic Review and Meta-Analyses of Word Production Abilities in Dysfunction of the Basal Ganglia: Stroke, Small Vessel Disease, Parkinson\u0026rsquo;s Disease, and Huntington\u0026rsquo;s Disease. Neuropsychol Rev. Springer; 2024. p. 1\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eLin F, Jia W, Li X, Chen Y, Wan M. Cognitive Profiles Stratified by Education Using Montreal Cognitive Assessment in Parkinson\u0026rsquo;s Disease Patients with Freezing of Gait. Neuropsychiatr Dis Treat. 2024;20:25\u0026ndash;34.\u003c/li\u003e\n\u003cli\u003eEklund M, Nuuttila S, Joutsa J, Jaakkola E, M\u0026auml;kinen E, Honkanen EA, et al. Diagnostic value of micrographia in Parkinson\u0026rsquo;s disease: a study with [123I]FP-CIT SPECT. J Neural Transm. 2022;129:895\u0026ndash;904.\u003c/li\u003e\n\u003cli\u003eElstein AS, Schwarz A. Evidence base of clinical diagnosis. Clinical problem solving and diagnostic decision making: Selective review of the cognitive literature. Br Med J. BMJ Publishing Group; 2002. p. 729\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eMarras C, Tr\u0026ouml;ster AI, Kulisevsky J, Stebbins GT. The tools of the trade: A state of the art \u0026lsquo;How to Assess Cognition\u0026rsquo; in the patient with Parkinson\u0026rsquo;s disease. Movement Disorders. 2014;29:584\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003ePirogovsky-Turk E, Moore RC, Filoteo JV, Litvan I, Song DD, Lessig SL, et al. Neuropsychiatric Predictors of Cognitive Decline in Parkinson Disease: A Longitudinal Study. American Journal of Geriatric Psychiatry. 2017;25:279\u0026ndash;89.\u003c/li\u003e\n\u003cli\u003ePagonabarraga J, Kulisevsky J, Llebaria G, Garc\u0026iacute;a-S\u0026aacute;nchez C, Pascual-Sedano B, Gironell A. Parkinson\u0026rsquo;s disease-cognitive rating scale: A new cognitive scale specific for Parkinson\u0026rsquo;s disease. Movement Disorders. 2008;23:998\u0026ndash;1005.\u003c/li\u003e\n\u003cli\u003eMarinus J, Visser M, Verwey N, Verhey F, Middelkoop H, Stiggelbout A, et al. Assessment of cognition in Parkinson\u0026rsquo;s disease. Neurology [Internet]. 2003;61:1222\u0026ndash;8. Available from: www.neurology.org\u003c/li\u003e\n\u003cli\u003eKalbe E, Calabrese P, Kohn N, Hilker R, Riedel O, Wittchen HU, et al. Screening for cognitive deficits in Parkinson\u0026rsquo;s disease with the Parkinson neuropsychometric dementia assessment (PANDA) instrument. Parkinsonism Relat Disord. 2008;14:93\u0026ndash;101.\u003c/li\u003e\n\u003cli\u003eKilsdonk E, Peute LW, Jaspers MWM. Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis. Int J Med Inform. 2017;98:56\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eTang CC, Poston KL, Eckert T, Feigin A, Frucht S, Gudesblatt M, et al. Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol [Internet]. 2010;9:149. Available from: www.thelancet.com/neurology\u003c/li\u003e\n\u003cli\u003eAl-Ashkar F, Mehra R, Mazzone PJ. Interpreting pulmonary function tests: Recognize the pattern, and the diagnosis will follow. Cleve Clin J Med. 2003;70:866\u0026ndash;81.\u003c/li\u003e\n\u003cli\u003eMcMahon L, Blake C, Lennon O. A systematic review and meta-analysis of respiratory dysfunction in Parkinson\u0026rsquo;s disease. Eur J Neurol. John Wiley and Sons Inc; 2023. p. 1481\u0026ndash;504.\u003c/li\u003e\n\u003cli\u003eWen MC, Chan LL, Tan LCS, Tan EK. Mild cognitive impairment in Parkinson\u0026rsquo;s disease: A distinct clinical entity? Transl Neurodegener. 2017;6.\u003c/li\u003e\n\u003cli\u003eSeppi K, Ray Chaudhuri K, Coelho M, Fox SH, Katzenschlager R, Perez Lloret S, et al. Update on treatments for nonmotor symptoms of Parkinson\u0026rsquo;s disease\u0026mdash;an evidence-based medicine review. Movement Disorders. John Wiley and Sons Inc; 2019. p. 180\u0026ndash;98.\u003c/li\u003e\n\u003cli\u003eChandler JM, Nair R, Biglan K, Ferries EA, Munsie LM, Changamire T, et al. Characteristics of Parkinson\u0026rsquo;s Disease in Patients with and without Cognitive Impairment. J Parkinsons Dis. 2021;11:1381\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eWillis AW, Roberts E, Beck JC, Fiske B, Ross W, Savica R, et al. Incidence of Parkinson disease in North America. NPJ Parkinsons Dis. 2022;8.\u003c/li\u003e\n\u003cli\u003eMmed Z, Cui Mmed YJ, Mmed Z, Mmed Y, Md S, Feng T, et al. Temporal trends in the prevalence of Parkinson\u0026rsquo;s disease from 1980 to 2023: a systematic review and meta-analysis [Internet]. Articles Lancet Healthy Longev. 2024. Available from: www.thelancet.com/\u003c/li\u003e\n\u003cli\u003eRay Dorsey E, Elbaz A, Nichols E, Abd-Allah F, Abdelalim A, Adsuar JC, et al. Global, regional, and national burden of Parkinson\u0026rsquo;s disease, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2018;17:939\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eMattson MP. Superior pattern processing is the essence of the evolved human brain. Front Neurosci. 2014;\u003c/li\u003e\n\u003cli\u003eCammisuli DM, Cammisuli SM, Fusi J, Franzoni F, Pruneti C. Parkinson\u0026rsquo;s Disease\u0026ndash;Mild Cognitive Impairment (PD-MCI): A Useful Summary of Update Knowledge. Front Aging Neurosci. Frontiers Media S.A.; 2019.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clinical reasoning, Parkinson's disease, Cognitive impairment, Montreal Cognitive Assessment, Qualitative research, Biomedical informatics","lastPublishedDoi":"10.21203/rs.3.rs-7329834/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7329834/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Parkinson’s disease (PD) is the second-most diagnosed age-related neurodegenerative disorder globally. PD pathology causes dysregulation of motor movement and for many, mild or minor cognitive impairment (PD-MCI). The most recommended global screening exam to detect PD-MCI is the Montreal Cognitive Assessment© (MoCA). Traditionally, the MoCA is scored according to guidelines and compared against a standardized cutoff, but clinical professionals additionally draw upon their observations of the patient’s performance to determine the score. To better understand how clinicians use the MoCA in real-world clinical settings, we employed the qualitative descriptive approach to identify performance patterns professionals utilize to assess the cognitive health of a person with PD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We curated retrospective data from nine people with PD-MCI to PD-Dementia. Each patient had one completed MoCA exam and one neuropsychological report containing health data. The assessments were organized into three groups of three and used in semi-structured interviews with six clinical professionals to gather at minimum two clinical opinions for each.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Three coders distilled, based on consensus, three clinically meaningful patterns from the interviews composed of features emphasized as vital by the interviewees for determining a person’s cognitive health. The derived features were from a patient’s performance on sections of the MoCA exam, sociodemographic and health data from the neuropsychological report, and dependent relationships between the assessments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study leveraged the popular MoCA exam to explore its real-world clinical use. Extracting these patterns clinicians recognized provides deeper insights into how they interpret cognitive health creating a blueprint for future efforts to tailor the exam for detecting cognitive impairment in people with PD.\u003c/p\u003e","manuscriptTitle":"A Qualitative Approach to Extract Diagnostic Patterns of Cognitive Impairment in Parkinson’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-03 08:46:46","doi":"10.21203/rs.3.rs-7329834/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-16T07:22:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-15T04:14:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202652533248418270237388787563885895242","date":"2026-03-29T16:37:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T15:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288231715136534356797442336232545997628","date":"2026-03-06T08:28:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235459141047214488217560941122844610039","date":"2025-12-11T16:54:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-01T14:31:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T10:59:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-18T02:24:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-13T23:34:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-13T23:31:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b66506f-ed54-4996-a2f8-00862f06c1f6","owner":[],"postedDate":"December 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58899666,"name":"Health sciences/Diseases"},{"id":58899667,"name":"Health sciences/Health care"},{"id":58899668,"name":"Health sciences/Neurology"},{"id":58899669,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-30T14:54:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-03 08:46:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7329834","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7329834","identity":"rs-7329834","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}