Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol

Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 24 September 2025 V1 Latest version Share on Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol Authors : Olivier Percie Du Sert 0000-0002-6283-2529 , Joseph Ghanem , Vanessa McGrory , Karyne Anselmo , Kelly Anderson 0000-0001-9843-404X , Srividya Iyer 0000-0001-5367-9086 , Ridha Joober , Jai Shah , Ashok Malla 0000-0002-5863-4191 , and Martin Lepage 0000-0003-4345-6502 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175870089.97534629/v1 287 views 149 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract While early intervention services (EIS) have demonstrated short-term benefits, the long-term maintenance of these gains remains largely unexplored. Despite overall better outcomes, individuals with first-episode psychosis exhibit significant variability in their course of recovery. Understanding the risk and protective factors that shape long-term outcome trajectories is essential to predicting and promoting sustained recovery. The Prevention and Early Intervention Program for Psychoses (PEPP-Montreal) is a well-established, high-fidelity EIS program operating within a universal healthcare system and an epidemiologically defined catchment area in South-West Montréal, Canada. Between 2003 and 2018, PEPP-Montreal conducted a detailed two-year longitudinal assessment of 689 individuals aged 14–35 with first-episode affective or non-affective psychosis. Here, we present the protocol for an extended 10-year follow-up study of social, mental, cognitive, and physical health outcomes, supplemented through linkage with health administrative databases to offer a holistic perspective on long-term outcome trajectories. The primary objective of the study is to model the heterogeneity of long-term trajectories across multiple outcome dimensions over the 10-year follow-up period using data-driven methods. This approach will help distinguish clinically meaningful subgroups, characterize their profiles, and identify early predictors of long-term outcomes while providing insight into the mechanisms of changes within trajectories. In particular, the study will assess whether trajectories shaped during the critical period are sustained over the long term. To our knowledge, this represents the most comprehensive investigation of long-term trajectories following EIS in North America and is expected to lay the groundwork for optimizing EIS and developing personalized interventions. Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol Olivier Percie du Sert 1, 2 , Joseph Ghanem 1, 3 , Vanessa McGrory 1, 4 , Karyne Anselmo 1 , Kelly K. Anderson 5 , Srividya Iyer 1, 2 , Ridha Joober 1, 2 , Jai Shah 1, 2 , Ashok Malla 1, 2 , Martin Lepage 1, 2 1 Douglas Research Centre, Montreal, Quebec, Canada 2 Department of Psychiatry, McGill University, Montreal, Quebec, Canada 3 Department of Psychology, McGill University, Montreal, Quebec, Canada 4 Department of Psychology, Université du Québec à Montréal, Montreal, Quebec, Canada 5 Departments of Epidemiology & Biostatistics and Psychiatry, Western University, Ontario Canada Corresponding author: Martin Lepage, PhD Douglas Mental Health University Institute 6875 LaSalle Blvd.,Montreal, Quebec H4H 1R3, Canada Tel.: +1 (514) 761-6131 ext. 4393 Fax: +1 (514) 888-4064 Email address: [email protected] ABSTRACT While early intervention services (EIS) have demonstrated short-term benefits, the long-term maintenance of these gains remains largely unexplored. Despite overall better outcomes, individuals with first-episode psychosis exhibit significant variability in their course of recovery. Understanding the risk and protective factors that shape long-term outcome trajectories is essential to predicting and promoting sustained recovery. The Prevention and Early Intervention Program for Psychoses (PEPP-Montreal) is a well-established, high-fidelity EIS program operating within a universal healthcare system and an epidemiologically defined catchment area in South-West Montréal, Canada. Between 2003 and 2018, PEPP-Montreal conducted a detailed two-year longitudinal assessment of 689 individuals aged 14–35 with first-episode affective or non-affective psychosis. Here, we present the protocol for an extended 10-year follow-up study of social, mental, cognitive, and physical health outcomes, supplemented through linkage with health administrative databases to offer a holistic perspective on long-term outcome trajectories. The primary objective of the study is to model the heterogeneity of long-term trajectories across multiple outcome dimensions over the 10-year follow-up period using data-driven methods. This approach will help distinguish clinically meaningful subgroups, characterize their profiles, and identify early predictors of long-term outcomes while providing insight into the mechanisms of changes within trajectories. In particular, the study will assess whether trajectories shaped during the critical period are sustained over the long term. To our knowledge, this represents the most comprehensive investigation of long-term trajectories following EIS in North America and is expected to lay the groundwork for optimizing EIS and developing personalized interventions. KEYWORDS: Clinical Protocols; Healthcare Outcomes; Longitudinal Studies; Long-Term Care; Mental Health Recovery; Psychotic Disorders . INTRODUCTION The first episode of psychosis (FEP) typically occurs during adolescence or early adulthood, derailing normal life trajectories 1 . Pioneering work from the pre-antipsychotic era 2-4 established the heterogeneity of long-term outcomes in schizophrenia. Subsequent studies in the post-antipsychotic era, coinciding with deinstitutionalization, similarly observed significant heterogeneity, with a minority of patients achieving positive outcomes 5-10 . The development of Early Intervention Services (EIS) for FEP in the late 1990s and early 2000s brought new hope for improving outcomes 11-19 . EIS are specialized and integrated programs meant to proactively reduce delays in treatment, reduce clinical symptoms and improve functioning. Tailored for a specific age group and stage of illness, EIS offer a multi-component package of evidence-based pharmacological and psychosocial interventions delivered through assertive case management 20 . The rationale for EIS is grounded in seminal research indicating that both short- and long-term outcomes are influenced by the duration of untreated psychosis (DUP), which may be modifiable through early detection and timely intervention 21-23 . Additionally, it has been suggested that long-term outcome trajectories are shaped within the first five years following the onset of FEP 3, 24, 25 . EIS have been developed with the assumption that the early phase of psychosis may be a critical period of cerebral and psychosocial plasticity, and a window of opportunity during which adequate treatment may confer more favourable long-term outcomes 26 . Multiple systematic and meta-analytical reviews have repeatedly demonstrated the short-term (2-5 years) superiority of EIS compared with regular care 27-33 . However, the long-term maintenance of these benefits remains largely unexplored 34 . Many studies assessing long-term outcomes of FEP beyond five years have followed cohorts established before the introduction of EIS 35-38 . Only a few have specifically examined the long-term outcomes of prototypical, high-fidelity EIS 34 as defined by Addington, et al., (2012) 20 . For instance, Mihalopoulos et al. (2009) reported lower positive symptoms at the 8-year follow-up of the Early Psychosis Prevention and Intervention Centre (EPPIC) program in Melbourne, compared to historical controls 39 , while the 15-year follow-up is ongoing 40 . In contrast, Chan et al. (2015) found no difference in positive and negative symptoms, nor in rates of clinical and functional remission at the 10-year follow-up of the Early Assessment Service for Young People with Psychosis (EASY) program in Hong Kong, compared to regular care . However, they did report lower depressive symptoms and suicidal behaviours along with fewer and shorter hospitalizations and longer periods of employment for patients who received EIS 41 . Similarly, Hegelstad et al. (2012) found no differences in clinical remission but reported higher employment rates at 10 years of the Early Treatment and Intervention in Psychosis (TIPS) in Stavanger 42 . Additionally, findings from the long-term follow-up of the OPUS trial in Copenhagen revealed no significant differences between EIS and treatment as usual in functioning, positive and negative symptoms, as well as clinical remission and recovery rates at 10 43 , and 20 years 33 . Long-term follow-up studies of EIS are scarce and the few that exist face challenges that limit the reproducibility and generalizability of their findings, making it difficult to draw definitive conclusions regarding the durability of EIS effects in the long-term. Studies are often constrained by small sample sizes, poor representativeness of the initial sample, and lack statistical power to account for the clinical heterogeneity within FEP samples. These limitations likely hinder the detection of subtle but meaningful long-term effects and may explain some of the inconsistencies observed across studies. Therefore, the assumption that intervening early has the potential to improve long-term outcomes still requires further investigation. In addition, most meta-analyses on long-term outcomes have failed to identify predictors of clinical and functional recovery 31, 33, 44 with the exception of DUP and treatment adherence 45-48 . Despite insight into some prognostic factors, none have proven sensitive or specific enough to reliably estimate an individual’s chance of recovery 49 . This not only highlights the potential for optimizing EIS but also underscores the considerable heterogeneity among individuals with FEP 45, 50 . For clinicians, this heterogeneity poses significant challenges in planning treatment and providing prognostic guidance. It implies that EIS may have markedly different effects on different individuals. Consequently, one-size-fits-all approaches are unlikely to succeed, emphasizing the need for personalized treatment strategies. By definition, such strategies cannot be developed without accounting for the intrinsic heterogeneity in FEP, and failing to do so is likely to yield inaccurate prognoses. Therefore, identifying individual characteristics and other factors associated with both favourable and unfavourable outcome trajectories is critical. Patient heterogeneity has prompted the exploration of subgroups defined by clinical or biological features. Early efforts to dissect heterogeneity included the delineation of Type I and Type II schizophrenia 51 as well as deficit and non-deficit schizophrenia 52 based on the prominence of positive and negative symptoms. Later, the clinical staging model sought to identify more clinically homogeneous subgroups along a neurodevelopmental continuum, ranging from clinical high risk to chronic and enduring schizophrenia 53 . More recently, the Research Domain Criteria (RDoC) 54, 55 and the Hierarchical Taxonomy Of Psychopathology (HiTOP) 56 57, 58 have proposed dimensional frameworks designed to address the limitations of traditional categorical diagnostic systems including within-disorder heterogeneity, with a focus on neurobiological, neurocognitive and psychopathological dimensions. Heterogeneity can be effectively addressed using data-driven approaches like longitudinal latent growth modelling (LGM). LGM captures both within- and between-individual variability, allowing it to model outcome trajectories with distinct patterns of change over time, and distinguish underlying difference between homogeneous subgroups of patients 59 . Such data driven approach may help with the provision of more precise prognostication and the selection of individualized intervention. Habtewold et al. (2020) conducted a comprehensive review on data-driven studies examining trajectories for positive, negative, and cognitive symptoms in schizophrenia spectrum disorders 50 . However, very few studies involved FEP cohorts and EIS, and even fewer had long-term follow-up. We present the protocol for an extended 10-year follow-up study of the PEPP-Montreal cohort, comprising a large sample of individuals with FEP who previously received 2-years of EIS. The primary objective of the study is to model the heterogeneity of long-term trajectories across multiple outcome domains over a 10-year follow-up period using data-driven methods. Specifically, the study aims to 1) Identify latent trajectories of functional, clinical, cognitive and healthcare outcomes; 2) Characterize the social, mental, cognitive, physical health, and healthcare profiles of individuals associated with each trajectory (e.g., disability, severity, comorbidity, and mortality), and further describe each trajectory in terms of its course (e.g., stability, progression, deterioration, or transition over time); and 3) Assess the prognostic value of these trajectories by identifying early risk and protective factors associated with trajectory membership, rates of change and long-term outcomes. METHODS Design The Prevention and Early Intervention Program for Psychosis (PEPP) is a well-established, high-fidelity EIS at the Douglas Mental Health University Institute in Montreal, Canada. Launched in 2003 as a publicly funded catchment area-based service covering a population of 350,000, PEPP-Montréal has served over 1100 patients to date. A detailed description of the clinical program, along with the 2- and 5-year outcomes, is reported elsewhere 11, 60-63 . This service was established as a clinical-research program and implemented a comprehensive assessment protocol between 2003 and 2018. During this period, 689 individuals with FEP were systematically characterized over the course of the two-year EIS follow-up. Assessments were conducted at baseline and at 1, 2, 3, 6, 9, 12, 18, 24 months and 5 years. Fidelity to the EIS delivery model was established through monitoring of case manager-to-patient ratios, minimum number of clinical contacts per month and the availability of a range of psychosocial interventions 64, 65 . Participants are now being invited to take part in an extended naturalistic observational study with a 10-year follow-up assessment. The secondary use of data, access to medical records and contact information, along with participant recontact without explicit prior consent received ethical approval in accordance with the Tri-Council Policy Statement of Ethical Conduct (articles 5.5A; 5.6) 66 . Recruitment Inclusion & exclusion criteria At the time of admission to PEPP-Montreal, patients met the following inclusion criteria: aged between 14 and 35 years; DSM-IV diagnosis of psychotic disorder, including both schizophrenia-spectrum or affective psychoses; no or minimal prior antipsychotic treatment (< 30 days). Excluded patients were those with organic or drug-induced psychosis, a history of a central nervous system disease, an IQ below 70, and inability to provide informed consent or speak either English or French fluently. All former patients are eligible to participation in the current study, regardless of remission status or concurrent substance use disorder. Tracing and contact strategy As the only EIS within the catchment area, PEPP-Montreal has the potential to provide representative sample. Recruiting a representative sample of patients who have received EIS is the most important and challenging aspect of the current protocol. Participant tracing and contact retrieval will be carried out using a stepwise strategy aligned with best practices 67, 68 and summarized in Figure 1 . First, deceased individuals will be identified through consultation of public death records. For remaining participants, residential addresses, phone numbers, and email addresses will be retrieved by searching electronic medical records, archives, and transfer documentation from the parent institute. Initial contact attempts will be made using the last known contact information, beginning with individuals who are still receiving services at the parent institute. Next, provincial health administrative databases will be consulted to determine the most recent region of residence (3-digit postal codes) and the corresponding health and social services region. Access to electronic medical records systems and archives will then requested from the relevant health and social services centres to update contact information when available. For remaining participants, public registries, such as curatorship, judicial, business and land ownership records may be consulted with the support of authorized information providers (i.e., LexisNexis) that aggregate such data, in an effort to locate individuals. Contact information will also be retrieved through online searches of social media accounts (e.g., LinkedIn, Facebook, X, Instagram) and other online profiles, using people-search engines such as Pipl, Canada411 and Google. To verify and confirm digital identity, information will be corroborated using participants’ first and last names, date of birth, and previous addresses. When all existing contact methods will have been exhausted, individuals will be contacted through private messaging systems on their most recently updated social media platform as a last resort, following established recommendations 68, 69 . Finally, targeted advertisements will be used to encourage self-referral, including posters and paid ads, both online and in the community, on platforms such as Google and Facebook, as well as in public transit spaces and health and social service settings. Recruitment is currently ongoing, with 88 participants already enrolled and a mean follow-up of 13.7 years [range: 6.4–22.4] from admission. Preliminary investigations revealed that over 70% of the sample reside within the health and social services region of Montreal, while approximately 30% still receive care directly at the parent institute. Recruitment is expected to be completed by early 2027. Consecutive patients previously admitted to PEPP-Montreal are being sent a generic invitation letter that does not disclose any confidential information related to mental health or specific services. The letter includes a QR code linking to a form where prospective participant can indicate their interest in the study and update their contact information. Letters are sent to the last known address, or an updated address if available. When possible, research assistants follow up with a phone call, making up to five attempts within one month at varying times of day. If necessary, further attempts are made at three, six months and 1 year before the participant is considered lost to follow-up. Due to ethical constraints, home visits and tracing through secondary contacts (e.g., friends, neighbours or employers) was not pursued. To maximize engagement and retention, participants are offered incentives, flexible scheduling, and alternative data collection methods, such as online interview, along with phone call reminders following published recommendations 70-72 . Assessment Given that the aetiology of psychotic disorders involves a range of sociodemographic, clinical, cognitive, biological, neurobiological and environmental factors, a multidimensional approach is expected to provide a better characterization of trajectories and predictive accuracy than using measures from a single data modality 73 74 . The measures and domains included in the protocol were informed by findings from the Meaningful Assessment Protocol (MAP), a scoping review that identified key domains, measures, and metrics in EIS research to support the harmonization of assessment practices 75 and facilitate comparisons. In line with the principles of measurement-based care and practice-based research, the current protocol balances replication of the original battery, to ensure consistency and comparability over time, with the integration of newer scales and methods. Similarly to the U.S. Early Psychosis Intervention Network’s Core Assessment Battery (CAB-EPINET) 76 , a harmonized protocol developed through expert consensus to assess key domains relevant to psychosis at the patient level, the current protocol includes assessments of social mental, cognitive, physical health, healthcare, and recovery. It includes clinician-rated and patient-reported outcome measures, as well as performance-based, biological measures and administrative data. The full assessment will be administered over three separate two-hour visits. The assessed domains are summarized in Figure 2 . A full description of the assessment protocol, including all scales and interviews administered at each time point, is provided in Table 1 . Social health Sociodemographic: information was collected at admission and updated at the 10-year follow-up. Socioeconomic status is assessed using the Hollingshead Four-Factor Index (SES) 77 , food security is measured with the Household Food Security Survey (HFSS) 78 , and area-level deprivation is estimated using the Material and Social Deprivation Index 79 . Functioning: Premorbid academic and social functioning from childhood through early adolescence is assessed retrospectively with the Premorbid Adjustment Scale (PAS) 80 . The Social and Occupational Functioning Assessment Scale (SOFAS) 81 is used to assess global functioning, independent of the severity of psychiatric symptoms. Records of curatorship along with the nature of the responsibilities will be obtained from the Quebec Public Register of Representation Measures. Occupational, role, and housing history as well as social contacts at follow-up is self-reported using the WHO Life Chart Schedule (LCS) 82 and Strauss-Carpenter Outcome Scale (SCOS) 83 . Perceived social support from family, friends, and significant others is measured with the Multidimensional Scale of Perceived Social Support (MSPSS) 84 . Mental health Prodrome, Pathways to care & Diagnosis: Information including prodromal symptoms and the duration of untreated psychosis and illness were collected using the Circumstances of Onset and Relapse Schedule (CORS) and validated operational criteria 61 . Diagnosis was initially established at admission using the SCID-IV 85 and confirmed at 1- and 10-year follow-up. Psychopathology: Assessments are repeated at 10-year follow-up including the severity of positive and negative symptoms is assessed using the Scale for Assessment of Positive (SAPS) 86 and Negative Symptoms (SANS) 86 . Anxiety, depression, and mania are evaluated using the Hamilton Anxiety Scale (HAS) 87 , the Calgary Depression Scale (CDS) 88 , and the Young Mania Rating Scale (YMRS) 89 , respectively. In addition, childhood trauma and post-traumatic stress symptoms are assessed using the Childhood Trauma Questionnaire (CTQ) and the PTSD Checklist for DSM-5 (PCL-5), and the Life-event checklist (LEC-5). Suicidal behaviours and history of previous suicide attempts are recorded with the Columbia suicide severity rating scale (C-SSRS) 90 . Insight is rated with the Scale to Assess Unawareness of Mental Disorder (SUMD) 91 , and substance use is documented using the Chemical Use, Abuse, and Dependence Scale (CUAD) 92 . Medication: Antipsychotic use was recorded throughout treatment at PEPP and is collected at the 10-year follow-up through medical records review, with dosages converted to chlorpromazine equivalents based on established guidelines 93 , while medication adherence is assessed through self-report using standard thresholds 94 . Motor side effects, including tardive dyskinesia, dystonia, parkinsonism, and akathisia, are evaluated using the Extrapyramidal Symptom Rating Scale (ESRS) 95 . Cognitive health Cognitive performance was assessed at baseline and one year across all seven MATRICS domains (i.e., verbal memory, visual memory, working memory, visual attention, processing speed, executive functioning, and social cognition) 96 and is re-evaluated at the 10-year follow-up using the computerized CogState Research Battery 97 . Physical health While we aim to provide a comprehensive overview of body systems and functions, we focus specifically on screening for metabolic syndrome, given its high prevalence in psychosis, the presence of multiple contributing risk factors, and its strong association with comorbid metabolic, cardiovascular, and pulmonary disorders 98 . Physical examination captures vital signs (blood pressure, heart and respiratory rates, oxygen saturation) and anthropometric measures (body mass index, waist-to-hip ratio, and waist-to-height ratio) to assess overweight/obesity and estimate body composition. Laboratory analyses include glycated hemoglobin (HbA1c) and fasting blood glucose to screen for diabetes, a standard lipid panel (total cholesterol, HDL, LDL, and triglycerides) for dyslipidemia, albumin-to-creatinine ratio to estimate the glomerular filtration rate, and C-reactive protein as an inflammatory marker. Cardiovascular risk is estimated using Framingham risk scores as per the Canadian Cardiovascular Society recommendations 99, 100 while the risk for Chronic Obstructive Pulmonary Disease (COPD) is estimated using the self-administered COPD population screener 101 . Behavioral and environmental risk factors are assessed including tobacco use with the Heaviness of Smoking Index (HSI) 102 , physical inactivity with the Simple Physical Activity Questionnaire (SIMPAQ) 103 , diet quality with the Mediterranean Diet Adherence Screener (MEDAS) 104 . Perceived physical health is measured using a single-item general self-rated health (GSRH) 105 and the Medical Outcomes Study Short Form-12 (SF-12). Healthcare & Administrative Data Given the expected loss to follow-up and the substantial gap between the 2/5- and 10-year assessments, health administrative data is used to supplement primary data collection and to provide continuous information for the entire cohort over the full follow-up period. This approach also reduces participant burden while providing reliable, real-world data that would otherwise be difficult to obtain via self-report. The data, collected and maintained by the Quebec Ministry of Health and Social Services (MSSS) and the Provincial Health Insurance Board (RAMQ), is accessed through the Quebec Institute of Statistics (ISQ), and has been shown to have good quality and reliability for research purposes 106 . Linkage with the PEPP cohort was performed using health insurance number, full name, parental names, sex, and date of birth, resulting in 95% of individuals successfully matched. Data were extracted from the date of entry at PEPP-Montreal until the date of 10-year follow-up (or the most recent date of data availability at the time of extraction). Health administrative data includes detailed information on emergency department visits, hospitalizations, visits to specialists and general practitioners, interventions from local community service centres, medication prescription (for those covered under the public drug plan), and mortality data, including date and causes of death. For each service, the databases include detailed information on the service providers, service dates, associated diagnoses, interventions performed, and related costs. Mental Health Care Resource Utilization (MHCRU) will be operationalized as the total number of outpatient psychiatric-related contacts with general practitioners, psychiatrists, and emergency departments, as well as the number and duration of psychiatric hospitalizations. These will be identified based on the provision of mental health services using ICD codes, in accordance with the CIHI Mental Health and Substance Use Diagnosis Code Groupings 107 . MHCRU will be computed as count variables for each follow-up year and as cumulative totals, beginning from the index date of admission to PEPP-Montreal services and ending at the date of data extraction or right-censored at 10 years. In addition, data were requested for three control groups: 1) Non-EIS controls: individuals who experienced FEP but never received EIS, 2) Mental health controls: individuals with any non-psychotic psychiatric disorder, and 3) Physical health controls: individuals with any physical health condition and no lifetime history of psychiatric diagnosis. Case identification was conducted using a validated algorithm 108, 109 , individuals were then randomly selected by ISQ statisticians and matched to the PEPP cohort based on sociodemographic characteristics, service catchment area, and time period of service use. Health administrative data will be key in determining the representativeness of our recruited sample and essential for characterizing care trajectories using the multidimensional ‘6W’ model 110 , which considers patient attributes and health conditions (‘who’ and ‘why’), types of healthcare providers (‘which’), care settings (‘where’), treatments received (‘what’), and timing of services (‘when’). Selection criteria for the control groups, including diagnostic codes, are detailed in Supplementary Table 1 . Turning points and Endpoints: Response, Resistance, Remission & Recovery 3-month: Early treatment response will be defined as a ≥50% reduction in the severity of positive and/or negative symptoms within the first three months of the follow-up at PEPP-Montreal 111 . 2-year: Early and late treatment resistance will be operationalized according to criteria established by the Treatment Response and Resistance in Psychosis (TRRIP) Working Group 112 . Clinical remission will be defined using the Remission in Schizophrenia Working Group (RSWG) criteria, as minimal symptom severity (i.e., scores ≤2 on all global items of the SAPS and SANS, excluding the attention subscale) sustained for at least six months 113 . Functional remission will be defined as achieving a SOFAS score >60 24, 81 . 5-year: Relapse will be defined as any emergency department visit or hospitalization for any psychiatric reason in individuals who had previously achieved clinical remission 114 . 10-year : Clinical recovery will require meeting criteria for clinical remission along with the absence of psychiatric hospitalizations or emergency department visits for mental health reasons over a continuous two-year period. Functional recovery will be defined as meeting criteria for functional remission, along with having meaningful social contact (Strauss-Carpenter Outcome Scale, item 2 score > 1), living independently, and being engaged in full-time employment, education, training, or homemaking for a minimum of two years, or being retired. Individuals were considered in full recovery if they met criteria for both clinical and functional recovery. Quality of life, including satisfaction with housing, occupation, relationships, and health, is evaluated using the Wisconsin Quality of Life Index (WQOL). Personal recovery, as an ongoing process, is measured using the Recovery Assessment Scale (RAS), which captures key dimensions such as hope, self-determination, and goal orientation 115 . ANALYSES Descriptive statistics including point estimates and confidence intervals will be calculated to summarize the data on all participants. Primary analyses will focus on modelling and characterizing trajectories of functioning (SOFAS), while secondary analyses will investigate trajectories of clinical symptoms, cognitive performance, and patterns of service use as well as their interrelationships using similar methodology. All analyses will be performed using R 116 and Mplus 117 software. The MplusLGM 118 and MplusAutomation package for R 119 will be used to automate the iterative model-fitting procedure and the manual 3-step approach for analysis of covariates. Statistical significance will be set at p <0.05. Missing data will be handled using full information maximum likelihood estimation when appropriate, along with sensitivity analyses to evaluate the impact of non-random missing data. Trajectory analyses Longitudinal latent growth modelling (LGM) will be used to examine long-term trajectories of outcomes 120 . Trajectory modelling will be conducted following the procedure outlined by Van Der Nest et al., (2020) 59 and the Framework to construct and interpret latent class trajectory modelling 121 . This involved fitting increasingly less constrained LGMs in a systematic fashion to determine the optimal and most parsimonious set of model parameters. First, a single-class growth curve model (GCM) will be estimated to represent the sample mean trajectory of outcome over time, against which subsequent models will be compared. Second, class enumeration will be performed by fitting a series of group-based trajectory models (GBTM) with an increasing number of classes, and fixed residual variance. The maximum number of latent classes to be tested will be guided by prior research findings 50 . Third, the model with the optimal class structure will be extended in a latent class growth analysis (LCGA) by allowing free estimation of the residual variance across time, classes, and both. Fourth, class-invariant and class-variant random effects will be added stepwise in growth mixture models (GMM) by allowing the variance and covariance of the growth factors to be estimated and to vary across class. To mitigate the risk of local maxima, each model will be rerun with twice the number of random starts (starting with a set of 500 and 500/4) until the best log-likelihood value is replicated within and between 2 runs. In addition, piecewise LGMs will be conducted to examine whether rates of change differ across distinct time intervals. This approach will allow for the estimation of separate growth factors for each time periods, testing potential turning points at 3 months, 1, 2 and 5 years. Informed by previous univariate models, multivariate LGMs will be explored, including joint-trajectory modelling with parallel and sequential process to investigate interrelationships between independent trajectories of functional and clinical symptoms, cognitive performance and service use, as well as 2-year, 5-year and 10-year trajectories. Conditional probability tables will be generated to estimate the likelihood of an individual following a given trajectory i , conditional on their membership in a different trajectory j . To provide a comprehensive view of the overall long-term course of psychosis, a multidimensional LGM will also be estimated combining trajectories of both functional and clinical outcomes. Finally, to account for variation in assessment timing due to rolling admissions, differing follow-up windows and gaps in timeline, sensitivity analyses will be conducted using individually-varying times of observations and freely estimated timescores. This approach improves model accuracy by reflecting the actual timing of measurements, reduces bias from assuming uniform intervals, and enhances the robustness of longitudinal inferences. Model selection will be determined according to the Bayesian information criterion (BIC), the scaled entropy (sE), the average posterior probabilities (APPA), and the bootstrap likelihood ratio test (BLRT). A significant BLRT indicates that a K class model has a significant better fit than a K-1 class model. Lower BIC values suggest a more parsimonious model, higher entropy (>0.5) values imply a greater classification certainty of individuals, while higher APPA (>0.7) are indicative of a good fit. Class separation will be estimated using degrees of separation (DoS > 0.5) and overlap coefficients (OVL < 0.8) 122 . Aside from fit statistics, interpretability will also be taken into consideration, and models with classes accounting for less than 5% of the sample will be rejected. Given the number of available timepoints, a cubic trend will be assumed and, the polynomial order for the best-fitting model will be refined by iteratively removing the highest non-significant term based on Wald tests. While heterogeneity in the overall distribution of the data is expected, normality will only be assessed within classes of the final model using the multivariate skewness and kurtosis test 123 . If required, corrected distributions will be used accordingly to accommodate excessive skewness or kurtosis such as provided by Mplus 124 . Models will be reported in accordance with the guidelines for reporting on latent trajectory studies checklist to ensure transparency, reproducibility, and methodological rigor. Analyses of covariates The association of trajectory membership with baseline predictors, time-varying covariates and distal outcomes will be investigated following a manual 3-step approach 125, 126 . First, an unconditional model with no covariates will be estimated including class enumeration, model selection and refining of polynomial order. Second, misclassification rates converted in logit values will be extracted from the best-fitting model. Third, covariates will be included in the model while manually fixing the misclassification rates, ensuring that class membership remains stable and is not influenced by the addition of new model parameters 127 . Covariates will be identified based on their relevance in the literature, particularly those associated with functional and clinical remission and recovery 44, 48, 50, 128 . Multinomial logistic regressions will be used to identify predictors of latent class membership. Odd ratios will be reported for the “better” trajectories in reference to the “worse” trajectory, as the exponentiation of the logistic coefficients. To further examine dynamic associations, mixture regression between the growth factors of parallel and sequential processes will be used to evaluate the effects of time-varying covariates on the rates of change within trajectories. Latent Transition Analysis (LTA) will be employed to estimate the probability of transitioning between classes over time, while identifying the specific timepoints at which covariates have the greatest influence on these transitions. For distal outcomes, Wald chi-square tests will assess differences in means and thresholds across latent trajectories endpoints. Standardized mean differences will be calculated as the ratio of estimated group mean differences to the within-sample standard deviation. Bonferroni corrections will be applied to control for multiple comparisons. Finally, a multivariable prognostic model will be constructed using an augmented backward elimination procedure, guided by both statistical significance and change-in-estimate criteria 129 , in accordance with the updated guidance for reporting clinical prediction models that use regression or machine learning methods (TRIPOD+AI) 130 . Statistical power and sample size Although simulation studies have provided useful insights into factors influencing model performance, there is no consensus on the minimum sample size requirements for LGM 131 . Several data features are known to compromise the accuracy of class enumeration, class assignment, and parameters estimation, including small sample sizes (<250) 132 , a limited number of time points (<4) 132 , low class separation, high rates of missing data, and increased model complexity 59 . A simulation study found that a minimum sample size of 200 was sufficient under conditions of complete data, four time points, and high class separation for a two-class GMM. In contrast, a sample size of 800 was required when data featured 20% missingness and low class separation, though this could be reduced to 300 with the inclusion of up to 10 time points 133 . Furthermore, incorporating covariates strongly associated with class membership has been shown to improve class separation and reduce sample size requirements 131 . Given earlier findings from the PEPP-Montreal cohort 134 , which identified 2- to 3-class solutions with high class separation for 2-year trajectories of functional and clinical outcomes in a sample of 689 individuals, and considering the planned inclusion of 4 to 10 time points and relevant covariates, a minimum sample size of 250-300 participants is expected to provide sufficient statistical power and reduce the risk of inaccurate class enumeration, misclassification, and biased parameter estimates. DATA MANAGEMENT & OPEN SCIENCE Research assistants, all of whom hold at least a bachelor’s degree in a mental health–related field, undergo comprehensive training provided by a research coordinator with a minimum of a master’s degree. Prior to conducting assessments, they are required to achieve satisfactory inter-rater reliability with expert consensus ratings. To maintain reliability over time, formal inter-rater training sessions are held twice annually, and reliability is monitored using intra-class correlation coefficients (ICCs). Continuous supervision is provided throughout the study, with weekly meetings to ensure strict adherence to protocol, and resolve any ambiguities in data collection. To minimize bias, particularly expectation bias, and preserve the validity of the assessments, raters remain blind to diagnostic information and previous clinical ratings. To ensure robust and transparent data management, the study utilizes the Castor Electronic Data Capture (EDC) platform, which facilitates secure data entry, documentation, and version control of the database. 135 . A quality control procedure is implemented to monitor data integrity, with routine checks for missing values and validation against predefined ranges where applicable. Data quality is assessed in near real-time, enabling the research team to promptly identify issues and, when necessary, request that a participant repeat a problematic measure, thereby ensuring data accuracy and completeness. The PEPP10+ Study is committed to open science practices by implementing data governance procedures aligned with the FAIR data principles (i.e., Findable, Accessible, Interoperable, and Reusable) 136, 137 . In partnership with Maelstrom Research 138 , the study adopts standardized approaches to documenting and disseminating metadata and data dictionaries to facilitate data discovery and harmonization. Researchers may request access to individual-level de-identified research data (excluding health administrative data which remain the propriety of the Quebec Government) collected during the study. Access will be granted upon appropriate approvals, in accordance with the PEPP-Montreal Early Psychosis Databank 139 Data Access Policy. Conclusion The PEPP10+ study is uniquely positioned to test a foundational assumption of EIS, that early intervention during the critical period following the onset of psychosis can yield lasting effects. Specifically, the study examines whether the clinical and functional trajectories shaped during the initial two years of EIS are sustained over the long term, thereby evaluating the durability of early gains well beyond the period of specialized care. PEPP-Montréal is a well-established, high-fidelity EIS, operating within a universal healthcare system and an epidemiologically defined catchment area. From the outset, it has integrated a robust clinical research program and implemented a comprehensive longitudinal assessment protocol, allowing for the detailed characterization of a large and representative, treated-incidence sample of individuals with FEP. This 10-year follow-up assessment spans social, mental, cognitive, and physical health dimensions, offering a holistic perspective on long-term outcomes. The 10-year follow-up assessment will be supplemented through linkage with health administrative databases, enabling continuous investigation of healthcare trajectories while helping to mitigate the impact of attrition. To our knowledge, this represents the most comprehensive investigation of long-term outcomes in the context of EIS in North America. Precision psychiatry is an emerging approach that has so far offered limited prognostic guidance to patients with FEP, and few practical recommendations for clinicians on how to tailor EIS to individual profiles and needs. By adopting a data-driven approach, the PEPP10+ study aims to capture the diversity of individual experiences and model the heterogeneity of long-term outcome trajectories. This will help identify clinically meaningful subgroups of patients and early predictors of long-term outcomes, while providing insight into the mechanisms and timing of changes within trajectories. In doing so, this study lays essential groundwork for the optimization of EIS and future development of personalized interventions. Authors’ contributions OPDS : Funding acquisition, Conceptualization, Methodology, Software, Writing - Original Draft. JG : Methodology, Writing - Original Draft. VM : Methodology, Writing - Original Draft. KA : Methodology, Project administration, Review & Editing. KKA : Funding acquisition, Review & Editing. SI : Conceptualization, Funding acquisition, Resources, Writing - Review & Editing. RJ : Conceptualization, Funding acquisition, Resources, Writing - Review & Editing. JS : Conceptualization, Funding acquisition, Resources, Writing - Review & Editing. AM : Conceptualization, Funding acquisition, Resources, Supervision, Writing - Review & Editing. ML : Conceptualization, Funding acquisition, Resources, Supervision, Writing - Review & Editing, Patience. Funding statement This study is supported by the Canadian Institutes of Health Research (180502). Salary awards include Canadian Institutes for Health Research ( JG, JS, ML ), the Canada Research Chairs program ( KKA, SI ), Fonds de la Recherche en Santé du Québec ( OPDS, VM, JS, ML ), James McGill Professorship ( ML ). The funding source had no role in the writing and publication of the manuscript. Conflict of interest disclosure RJ reports receipts of grants, speaker’s honoraria, and consultant’s honoraria from AstraZeneca, Bristol Myers Squibb, HLS Therapeutics, Janssen, Lundbeck Canada, Myelin and Associates, Otsuka Canada, Perdue, Pfizer, Shire, and Sunovion. AM reports receipts of grants, speaker’s honoraria, and consultant’s honoraria from Bristol Myers Squibb Canada, and Otsuka Lundbeck Alliance. ML reports receipts of grants from Otsuka Lundbeck Alliance, diaMentis, and Roche, personal fees from Otsuka Canada, Lundbeck Canada, and Bohringher Ingelheim, as well as grants and personal fees from Janssen. The other authors declare no conflicts of interest. Acknowledgments We extend our gratitude to all PEPP-Montreal participants whose valuable contributions made this project possible. Our appreciation also goes to the PEPP-Montreal staff for their efforts in recruitment and data collection throughout the years. Figure 1. Tracing Strategies for Locating and Reconnecting with Participants. Figure 2. Overview of assessment protocol. Footnote: ASCVD: Atherosclerotic Cardiovascular Disease; bpm: Beats per Minute; DUI: Duration of Untreated Illness; DUP: Duration of Untreated Psychosis; eGFR: Estimated Glomerular Filtration Rate; GP: General Practitioner; HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; rpm: Respirations per Minute; SES: Socioeconomic Status; SPE: Specialized Psychiatric Evaluation; sx: Symptoms; TC: Total Cholesterol; TG: Triglycerides. Table 1. Scales and Assessment Measures. SOCIAL HEALTH Sociodemographics Age, sex, ethnicity, education (SD) SD SD SD Socioeconomic Hollingshead 4-Factor Index (SES) SES SES SES Household Food Security Survey Module (HFSSM) HFSSM Material/Social deprivation index (MSDI) MSDI MSDI MSDI MSDI Functioning Premorbid Adjustment Scale (PAS) PAS Strauss-Carpenter (SC) SC SC SC SC SC Social/Occupational Functioning Assessment Scale (SOFAS) SOFAS SOFAS SOFAS SOFAS SOFAS Multidimensional Scale of Perceived Social Support (MSPSS) MSPSS MSPSS MSPSS MSPSS WHO Life Chart Schedule (LCS) LCS MENTAL HEALTH Prodrome & Pathways to care Course of Onset & Relapse Schedule (CORS) CORS Psychopathology Structured Clinical Interview for DSM-IV (SCID-IV) SCID SCID SCID Positive and Negative Syndrome Scale (PANSS) PANSS PANSS PANSS PANSS PANSS PANSS PANSS Scale for Assessment of Positive Symptoms (SAPS) SAPS SAPS SAPS SAPS SAPS SAPS SAPS Scale for Assessment of Negative Symptoms (SANS) SANS SANS SANS SANS SANS SANS SANS Hamilton Anxiety Scale (HAS) HAS HAS HAS HAS HAS HAS Calgary Depression Scale (CDS) CDS CDS CDS CDS CDS CDS Young Mania Rating Scale (YMRS) YMRS YMRS YMRS YMRS YMRS YMRS Scale to Assess Unawareness of Mental Disorder (SUMD) SUMD SUMD SUMD SUMD SUMD SUMD The Chemical Use, Abuse, and Dependence Scale (CUAD) CUAD CUAD CUAD CUAD CUAD CUAD CUAD Childhood Trauma questionnaire (CTQ) CTQ PTSD Checklist for DSM5 (PCL-5) PCL-5 Life Events Checklist for DSM-5 (LEC-5) LEC-5 Columbian Suicide Severity Rating Scale (CSSRS) CSSRS COGNITIVE HEALTH Cognition Wechsler Abbreviated Scale of Intelligence (WASI) WASI-II CogState Research Battery (CSRB) CSRB CSRB CSRB PHYSICAL HEALTH Physical examination Vital signs - BP, HR, RR, O2sat (VS) VS Anthropometrics - height, weight, waist ratios (AMtrics) AMtrics AMtrics AMtrics AMtrics AMtrics AMtrics Extrapyramidal Symptoms Rating Scale (ESRS) ESRS ESRS ESRS ESRS ESRS ESRS Blood Test Lipids profile - TC, LDL, HDL, TGs (Lipids) Lipids Glucose profile - fasting, HbA1c (Glucids) Glucids Kidney profile - creatinine/albumin (eGFR) eGFR Inflamation profile - C-reactive Protein (CRP) CRP Cardiovascular & Pulmonary Compressive-Obstructive Pulmonary Disorders (COPD) COPD Atherosclerotic Cardiovascular Disease Risk (ASCVD) ASCVD Health Behaviors Short Form Health Survey (SF-12) SF-12 Heaviness of smoking Index (HSI) HIS Mediterranean Diet Adherence Screener (MEDAS) MEDAS Simple Physical Activity Index Questionnaire (SIMPAQ) SIMPAQ Quebec Health Insurance Board (RAMQ) Insured Persons Information (FIPA) FIPA Medical services (MOD) MOD Pharmaceutical services (MED) MED Ministry of Health and Social Services (MSSS) Register of Demographic Events (RED) RED Clientele and Services Information System for CSSSs (I-CLSC) I-CLSC Emergency Database (BDCU) BDCU Data for the Study of Hospital Clientele (MED-ECHO) MED-ECHO Hospital Performance (APR-DRG) APR-DRG ENDPOINTS Treatment Response (TRRIP) TRRIP Treatment Resistance - early & late (TRRIP) early - TR late - TR late – TR Remission - clinical & functional (RSWG) RSWG RSWG Relapse (REL) REL REL Recovery - clinical & functional (RECOV) RECOV Wisconsin Quality of Life (WQoL) WQoL Personal Recovery Assessment Scale (RAS) RAS https://castoredc.com References 1. Crespo-Facorro B, Such P, Nylander AG, et al. The burden of disease in early schizophrenia - a systematic literature review. Current Medical Research and Opinion Jan 2021;37(1):109-121. 2. Bleuler E. Dementia paecox oder die Gruppe der Schizophrenien. Hnadbuch der Psychiatrie 1911. 3. Bleuler M. The schizophrenic disorders: Long-term patient and family studies . New Haven, Connecticut, U.S: Yale University Press; 1978. 4. Ciompi L. Learning from outcome studies: Toward a comprehensive biological-psychosocial understanding of schizophrenia. Schizophrenia Research 1988/11/01/ 1988;1(6):373-384. 5. Carpenter WT, Kirkpatrick B. The Heterogeneity of the Long-Term Course of Schizophrenia. Schizophrenia Bulletin 1988;14(4):645-652. 6. Angst J. European Long-term Followup Studies of Schizophrenia. Schizophrenia Bulletin 1988;14(4):501-513. 7. McGlashan TH. A Selective Review of Recent North American Long-term Followup Studies of Schizophrenia. Schizophrenia Bulletin 1988;14(4):515-542. 8. Sartorius N, Gulbinat W, Harrison G, Laska E, Siegel C. Long-term follow-up of schizophrenia in 16 countries. Social Psychiatry and Psychiatric Epidemiology 1996;31(5):249-258. 9. Jobe TH, Harrow M. Long-Term Outcome of Patients with Schizophrenia: A Review. The Canadian Journal of Psychiatry 2005;50(14):892-900. 10. Peritogiannis V, Gogou A, Samakouri M. Very long-term outcome of psychotic disorders. International Journal of Social Psychiatry 2020;66(7):633-641. 11. Malla A, Norman R, McLean T, Scholten D, Townsend L. A Canadian programme for early intervention in non-affective psychotic disorders. Australian and New Zealand Journal of Psychiatry Aug 2003;37(4):407-413. 12. McGorry PD, Edwards J, Mihalopoulos C, Harrigan SM, Jackson HJ. EPPIC: an evolving system of early detection and optimal management. Schizophrenia bulletin 1996;22(2):305-326. 13. Jørgensen P, Nordentoft M, Abel MB, Gouliaev G, Jeppesen P, Kassow P. Early detection and assertive community treatment of young psychotics: the Opus Study. Social Psychiatry and Psychiatric Epidemiology 2000;35(7):283-287. 14. Johannessen JO, Larsen TK, Horneland M, et al. The Tips Project: A systematic Program to reduce Duration of Untreated Psychosis in First Episode Schizophrenia. Springer Netherlands; 2001:151-166. 15. Craig TK, Garety P, Power P, Rahaman N, Colbert S, Fornells-Ambrojo M, Dunn G. The Lambeth Early Onset (LEO) Team: randomised controlled trial of the effectiveness of specialised care for early psychosis. Bmj 2004;329(7474):1067. 16. Kuipers E, Holloway F, Rabe-Hesketh S, Tennakoon L. An RCT of early intervention in psychosis: Croydon Outreach and Assertive Support Team (COAST). Social psychiatry and psychiatric epidemiology 2004;39(5):358-363. 17. Chen. Early psychosis in Hong Kong: The EASY program. Schizophrenia Research 2004;70(1). 18. Sim K, Swapna V, Mythily S, Mahendran R, Kua EH, McGorry P, Chong SA. Psychiatric comorbidity in first episode psychosis: the Early Psychosis Intervention Program (EPIP) experience. Acta Psychiatrica Scandinavica 2004;109(1):23-29. 19. Addington J, Addington D. Early intervention for psychosis: The Calgary early psychosis treatment and prevention program. 2001. 20. Addington DE, McKenzie E, Norman R, Wang J, Bond GR. Essential Evidence-Based Components of First-Episode Psychosis Services. Psychiatric Services 2013;64(5):452-457. 21. Penttilä M, Jääskeläinen E, Hirvonen N, Isohanni M, Miettunen J. Duration of untreated psychosis as predictor of long-term outcome in schizophrenia: systematic review and meta-analysis. The British Journal of Psychiatry 2014;205(2):88-94. 22. Howes OD, Whitehurst T, Shatalina E, et al. The clinical significance of duration of untreated psychosis: an umbrella review and random‐effects meta‐analysis. World Psychiatry 2021;20(1):75-95. 23. Catalan A, Salazar De Pablo G, Aymerich C, et al. ”Short” Versus “Long” Duration of Untreated Psychosis in People with First-Episode Psychosis: A Systematic Review and Meta-Analysis of Baseline Status and Follow-Up Outcomes. Schizophrenia Bulletin 2024. 24. Harrison G, Hopper K, Craig T, et al. Recovery from psychotic illness: A 15- and 25-year international follow-up study. British Journal of Psychiatry 2001;178(6):506-517. 25. Birchwood M, Macmillan F. Early intervention in schizophrenia. Australian & New Zealand Journal of Psychiatry 1993;27(3):374-378. 26. Birchwood M, Todd P, Jackson C. Early intervention in psychosis: the critical period hypothesis. The British journal of psychiatry 1998;172(S33):53-59. 27. Harvey P-O, Lepage M, Malla A. Benefits of Enriched Intervention Compared with Standard Care for Patients with Recent-Onset Psychosis: A Metaanalytic Approach. The Canadian Journal of Psychiatry 2007;52(7):464-472. 28. Bird V, Premkumar P, Kendall T, Whittington C, Mitchell J, Kuipers E. Early intervention services, cognitive–behavioural therapy and family intervention in early psychosis: systematic review. The British Journal of Psychiatry 2010;197(5):350-356. 29. Marshall M, Rathbone J. Early intervention for psychosis. Cochrane Database Syst Rev Jun 15 2011(6):CD004718. 30. Álvarez-Jiménez M, Parker AG, Hetrick SE, McGorry PD, Gleeson JF. Preventing the Second Episode: A Systematic Review and Meta-analysis of Psychosocial and Pharmacological Trials in First-Episode psychosis. Schizophrenia Bulletin 2011;37(3):619-630. 31. Correll CU, Galling B, Pawar A, et al. Comparison of Early Intervention Services vs Treatment as Usual for Early-Phase Psychosis: A Systematic Review, Meta-analysis, and Meta-regression. JAMA Psychiatry Jun 1 2018;75(6):555-565. 32. Puntis S, Minichino A, De Crescenzo F, Cipriani A, Lennox B, Harrison R. Specialised early intervention teams for recent-onset psychosis. Cochrane Database Syst Rev Nov 2 2020;11(11):CD013288. 33. Hansen HG, Starzer M, Nilsson SF, Hjorthøj C, Albert N, Nordentoft M. Clinical Recovery and Long-Term Association of Specialized Early Intervention Services vs Treatment as Usual Among Individuals With First-Episode Schizophrenia Spectrum Disorder: 20-Year Follow-up of the OPUS Trial. JAMA psychiatry 2023;80(4):371-379. 34. Chan SKW, Chan HYV, Devlin J, et al. A systematic review of long-term outcomes of patients with psychosis who received early intervention services. International Review of Psychiatry 2019;31(5-6):425-440. 35. Volavka J, Vevera J. Very long-term outcome of schizophrenia. International Journal of Clinical Practice 2018;72(7):e13094. 36. Peralta V, García de Jalón E, Moreno-Izco L, Peralta D, Janda L, Sánchez-Torres AM, Cuesta MJ, M SGBAG-BGHRLRFLPSRMRAZ. Long-term outcomes of first-admission psychosis: a naturalistic 21-year follow-up study of symptomatic, functional and personal recovery and their baseline predictors. Schizophrenia Bulletin 2022;48(3):631-642. 37. O’Keeffe D, Hannigan A, Doyle R, et al. The iHOPE-20 study: Relationships between and prospective predictors of remission, clinical recovery, personal recovery and resilience 20 years on from a first episode psychosis. Australian and New Zealand Journal of Psychiatry Nov 2019;53(11):1080-1092. 38. Tramazzo S, Lian W, Ajnakina O, Carlson G, Bromet E, Kotov R, Jonas K. Long-term course of remission and recovery in psychotic disorders. American Journal of Psychiatry 2024;181(6):532-540. 39. Mihalopoulos C, Harris M, Henry L, Harrigan S, McGorry P. Is early intervention in psychosis cost-effective over the long term? Schizophr Bull Sep 2009;35(5):909-918. 40. Cotton S, Filia K, Watson A, et al. A protocol for the first episode psychosis outcome (FEPOS): ≥15 year follow‐up after treatment at the Early Psychosis Prevention and Intervention Centre, Melbourne, Australia. Early Intervention in Psychiatry 2021-08-20 2021. 41. Chan SKW, So HC, Hui CLM, et al. 10-year outcome study of an early intervention program for psychosis compared with standard care service. Psychological medicine 2015;45(6):1181-1193. 42. Hegelstad WT, Larsen TK, Auestad B, et al. Long-term follow-up of the TIPS early detection in psychosis study: effects on 10-year outcome. The American journal of psychiatry Apr 2012;169(4):374-380. 43. Secher RG, Hjorthoj CR, Austin SF, Thorup A, Jeppesen P, Mors O, Nordentoft M. Ten-year follow-up of the OPUS specialized early intervention trial for patients with a first episode of psychosis. Schizophr Bull May 2015;41(3):617-626. 44. Molstrom IM, Nordgaard J, Urfer-Parnas A, Handest R, Berge J, Henriksen MG. The prognosis of schizophrenia: A systematic review and meta-analysis with meta-regression of 20-year follow-up studies. Schizophrenia Research Dec 2022;250:152-163. 45. Lally J, Ajnakina O, Stubbs B, Cullinane M, Murphy KC, Gaughran F, Murray RM. Remission and recovery from first-episode psychosis in adults: systematic review and meta-analysis of long-term outcome studies. The British Journal of Psychiatry 2017:bjp. bp. 117.201475. 46. Catalan A, Richter A, Salazar De Pablo G, et al. Proportion and predictors of remission and recovery in first-episode psychosis: Systematic review and meta-analysis. European Psychiatry 2021;64(1):1-24. 47. Jaaskelainen E, Juola P, Hirvonen N, McGrath JJ, Saha S, Isohanni M, Veijola J, Miettunen J. A systematic review and meta-analysis of recovery in schizophrenia. Schizophr Bull Nov 2013;39(6):1296-1306. 48. van Dee V, Schnack HG, Cahn W. Systematic review and meta-analysis on predictors of prognosis in patients with schizophrenia spectrum disorders: An overview of current evidence and a call for prospective research and open access to datasets. Schizophrenia Research 2023/04/01/ 2023;254:133-142. 49. Dazzan P, Lappin JM, Heslin M, et al. Symptom remission at 12-weeks strongly predicts long-term recovery from the first episode of psychosis. Psychological medicine 2019:1-11. 50. Habtewold TD, Rodijk LH, Liemburg EJ, Sidorenkov G, Boezen HM, Bruggeman R, Alizadeh BZ. A systematic review and narrative synthesis of data-driven studies in schizophrenia symptoms and cognitive deficits. Transl Psychiatry Jul 21 2020;10(1):244. 51. Crow TJ. The Two-syndrome Concept: Origins and Current Status. Schizophrenia Bulletin 1985;11(3):471-488. 52. Carpenter Jr WT. The deficit syndrome. The American journal of psychiatry 1994;151(3):327-329. 53. McGorry PD, Purcell R, Hickie IB, Yung AR, Pantelis C, Jackson HJ. Clinical staging: a heuristic model for psychiatry and youth mental health. Medical Journal of Australia 2007;187(S7). 54. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry 2010;167(7):748-751. 55. Insel TR. The NIMH Research Domain Criteria (RDoC) Project: Precision Medicine for Psychiatry. American Journal of Psychiatry 2014;171(4):395-397. 56. Kotov R, Krueger RF, Watson D, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to traditional nosologies. Journal of Abnormal Psychology 2017;126(4):454-477. 57. Kotov R, Jonas KG, Carpenter WT, et al. Validity and utility of Hierarchical Taxonomy of Psychopathology (HiTOP): I. Psychosis superspectrum. World Psychiatry 2020;19(2):151-172. 58. Kotov R, Krueger RF, Watson D, et al. The Hierarchical Taxonomy of Psychopathology (HiTOP): A Quantitative Nosology Based on Consensus of Evidence. Annual Review of Clinical Psychology 2021;17(1):83-108. 59. Van Der Nest G, Lima Passos V, Candel MJJM, Van Breukelen GJP. An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software. Advances in Life Course Research 2020;43:100323. 60. Iyer S, Jordan G, MacDonald K, Joober R, Malla A. Early Intervention for Psychosis A Canadian Perspective. Journal of Nervous and Mental Disease May 2015;203(5):356-364. 61. Malla A, Norman R, Schmitz N, Manchanda R, Bechard-Evans L, Takhar J, Haricharan R. Predictors of rate and time to remission in first-episode psychosis: a two-year outcome study. Psychological medicine May 2006;36(5):649-658. 62. Malla A, Joober R, Iyer S, et al. Comparing three‐year extension of early intervention service to regular care following two years of early intervention service in first‐episode psychosis: a randomized single blind clinical trial. World Psychiatry 2017;16(3):278-286. 63. Malla A, Jordan G, Joober R, et al. A controlled evaluation of a targeted early case detection intervention for reducing delay in treatment of first episode psychosis. Social Psychiatry and Psychiatric Epidemiology 2014;49(11):1711-1718. 64. Nolin M, Malla A, Tibbo P, Norman R, Abdel-Baki A. Early Intervention for Psychosis in Canada. The Canadian Journal of Psychiatry 2016;61(3):186-194. 65. Bertulies-Esposito B, Iyer S, Abdel-Baki A. The Impact of Policy Changes, Dedicated Funding and Implementation Support on Early Intervention Programs for Psychosis. The Canadian Journal of Psychiatry 2022;67(8):585-597. 66. Canadian Institutes of Health Research NSaERCoC, and social sciences and humanities research council of Canada. Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans; 2022. 67. Ou S-R, Mondi CF, Yoo S, Park K, Warren B, Reynolds AJ. Thirty years later: Locating and interviewing participants of the Chicago Longitudinal Study. Early Childhood Research Quarterly 2020;51:1-13. 68. Dimakos C, Loomis C, Gilmer A, Wright C, Christens BD, Pelletier J, Peters RD. Locating longitudinal study participants 10 years after last contact: Contemporary approaches to sample retention. Journal of Community Psychology 2023;51(4):1540-1559. 69. Andrawes RR, Yousufzai SJ, Mattin SS, Elliott S, Barakat C. Tracing participants for longitudinal environmental health research using social networking sites: a pilot study. Exploration of Digital Health Technologies 2024:145-156. 70. Deckler E, Ferland M, Brazis S, Mayer MR, Carlson M, Kantrowitz JT. Challenges and Strategies for the Recruitment of Patients With Schizophrenia in a Research Setting. International Journal of Neuropsychopharmacology 2022;25(11):924-932. 71. Teague S, Youssef GJ, Macdonald JA, et al. Retention strategies in longitudinal cohort studies: a systematic review and meta-analysis. BMC Medical Research Methodology 2018;18(1). 72. Ewing SWF, Chang L, Cottler LB, Tapert SF, Dowling GJ, Brown SA. Approaching retention within the ABCD study. Developmental cognitive neuroscience 2018;32:130-137. 73. Coutts F, Koutsouleris N, McGuire P. Psychotic disorders as a framework for precision psychiatry. Nature Reviews Neurology 2023. 74. Kas MJH, Penninx BWJH, Knudsen GM, et al. Precision psychiatry roadmap: towards a biology-informed framework for mental disorders. Molecular Psychiatry 2025. 75. Ferrari M, Pawliuk N, Pope M, MacDonald K, Boruff J, Shah J, Malla A, Iyer SN. A scoping review of measures used in early intervention services for psychosis. Psychiatric Services 2023;74(5):523-533. 76. George P, Rosenblatt A, Daley T, Ghose SS, O’Brien J, Rajapaksa S, Saavedra D. The early psychosis intervention network (EPINET) national core assessment battery: building the foundation for a learning health care partnership. Paper presented at: the American Public Health Association’s VIRTUAL Annual Meeting and Expo. American Public Health Association, 2020. 77. Hollingshead AB. Four factor index of social status. New Haven, Connecticut, U.S: Yale journal of sociology; 1975. 78. Gulliford MC, Nunes C, Rocke B. The 18 Household Food Security Survey items provide valid food security classifications for adults and children in the Caribbean. BMC Public Health 2006;6(1):26. 79. Institut National de Santé Publique du Québec. Index of material and social deprivation compiled by the Bureau d’information et d’études en santé des populations (BIESP) from 1991, 1996, 2001, 2006, 2011, 2016 and 2021 Canadian Census data: INSPQ Gouvernement du Québec; 2021. 80. Cannon-Spoor HE, Potkin SG, Wyatt RJ. Measurement of premorbid adjustment in chronic schizophrenia. Schizophr Bull 1982;8(3):470-484. 81. Morosini P, Magliano L, Brambilla L, Ugolini S, Pioli R. Development, reliability and acceptability of a new version of the DSM‐IV Social and Occupational Functioning Assessment Scale (SOFAS) to assess routine social funtioning. Acta Psychiatrica Scandinavica 2000;101(4):323-329. 82. Linszen D, Lenior M., de Haan L., Dingemans P., Gersons, B. Life Chart Schedule. British Journal of Psychiatry 1998;172:84-89. 83. Strauss JS, Carpenter WT. The prediction of outcome in schizophrenia: I. Characteristics of outcome. Archives of General Psychiatry 1972;27(6):739-746. 84. Zimet GD, Dahlem NW, Zimet SG, Farley GK. The multidimensional scale of perceived social support. Journal of personality assessment 1988;52(1):30-41. 85. First MB. Structured clinical interview for DSM-IV axis I disorders. Biometrics Research Department 1997. 86. Andreasen NC. Scale for the Assessment of Positive Symptoms (SAPS) . Iowa City: University of Iowa; 1984. 87. Riskind JH, Beck AT, Brown G, Steer RA. Taking the measure of anxiety and depression. Validity of the reconstructed Hamilton scales. J Nerv Ment Dis Aug 1987;175(8):474-479. 88. Addington D, Addington J, Maticka-Tyndale E. Assessing depression in schizophrenia: the Calgary Depression Scale. Br J Psychiatry Suppl Dec 1993(22):39-44. 89. Young RC, Biggs JT, Ziegler VE, Meyer DA. A Rating Scale for Mania: Reliability, Validity and Sensitivity. British Journal of Psychiatry 1978;133(5):429-435. 90. Posner K, Brown GK, Stanley B, et al. The Columbia-Suicide Severity Rating Scale: initial validity and internal consistency findings from three multisite studies with adolescents and adults. The American journal of psychiatry Dec 2011;168(12):1266-1277. 91. Amador XF, Strauss DH, Yale SA, Flaum MM, Endicott J, Gorman JM. Assessment of insight in psychosis. American journal of Psychiatry 1993;150:873-873. 92. McGovern M, Morrison D. The Chemical Use, Abuse, and Dependence Scale (CUAD). Rationale, reliability, and validity. Journal of substance abuse treatment 1992;9(1):27. 93. Leucht S, Crippa A, Siafis S, Patel MX, Orsini N, Davis JM. Dose-Response Meta-Analysis of Antipsychotic Drugs for Acute Schizophrenia. American Journal of Psychiatry 2020;177(4):342-353. 94. Velligan DI, Lam YWF, Glahn DC, Barrett JA, Maples NJ, Ereshefsky L, Miller AL. Defining and Assessing Adherence to Oral Antipsychotics: A Review of the Literature. Schizophrenia Bulletin 2005;32(4):724-742. 95. Chouinard G, Margolese HC. Manual for the Extrapyramidal Symptom Rating Scale (ESRS). Schizophrenia Research 2005/07/15/ 2005;76(2):247-265. 96. Nuechterlein KH, Green MF, Kern RS, et al. The MATRICS Consensus Cognitive Battery, part 1: test selection, reliability, and validity. American Journal of Psychiatry 2008;165(2):203-213. 97. Pietrzak RH, Olver J, Norman T, Piskulic D, Maruff P, Snyder PJ. A comparison of the CogState Schizophrenia Battery and the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Battery in assessing cognitive impairment in chronic schizophrenia. J Clin Exp Neuropsychol Oct 2009;31(7):848-859. 98. Rodrigues M, Wiener JC, Stranges S, Ryan BL, Anderson KK. The risk of physical multimorbidity in people with psychotic disorders: A systematic review and meta-analysis. Journal of Psychosomatic Research 2021;140:110315. 99. Grundy SM, Cleeman JI, Merz CN, et al. Implications of recent clinical trials for the National Cholesterol Education Program Adult Treatment Panel III guidelines. Circulation Jul 13 2004;110(2):227-239. 100. Pearson GJ, Thanassoulis G, Anderson TJ, et al. 2021 Canadian Cardiovascular Society guidelines for the management of dyslipidemia for the prevention of cardiovascular disease in adults. Canadian journal of cardiology 2021;37(8):1129-1150. 101. Martinez FJ, Raczek AE, Seifer FD, et al. Development and Initial Validation of a Self-Scored COPD Population Screener Questionnaire (COPD-PS). COPD: Journal of Chronic Obstructive Pulmonary Disease 2008;5(2):85-95. 102. John U, Meyer C, Schumann A, Hapke U, Rumpf HJ, Adam C, Alte D, Ludemann J. A short form of the Fagerstrom Test for Nicotine Dependence and the Heaviness of Smoking Index in two adult population samples. Addict Behav Aug 2004;29(6):1207-1212. 103. Wareham NJ, Jakes RW, Rennie KL, Schuit J, Mitchell J, Hennings S, Day NE. Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public health nutrition 2003;6(4):407-413. 104. Papadaki A, Johnson L, Toumpakari Z, et al. Validation of the English Version of the 14-Item Mediterranean Diet Adherence Screener of the PREDIMED Study, in People at High Cardiovascular Risk in the UK. Nutrients Jan 28 2018;10(2). 105. Jurges H, Avendano M, Mackenbach JP. Are different measures of self-rated health comparable? An assessment in five European countries. European journal of epidemiology 2008;23(12):773-781. 106. Tamblyn R, Lavoie G, Petrella L, Monette J. The use of prescription claims databases in pharmacoepidemiological research: the accuracy and comprehensiveness of the prescription claims database in Quebec. Journal of clinical epidemiology 1995;48(8):999-1009. 107. Canadian Institute for Health Information. CIHI Mental Health and Substance Use Diagnosis Code Groupings for Selected Indicators. Ottawa, ON: CIHI; 2024. 108. Anderson KK, Norman R, MacDougall AG, Edwards J, Palaniyappan L, Lau C, Kurdyak P. Estimating the incidence of first-episode psychosis using population-based health administrative data to inform early psychosis intervention services. Psychological medicine Sep 2019;49(12):2091-2099. 109. Kurdyak P, Lin E, Green D, Vigod S. Validation of a Population-Based Algorithm to Detect Chronic Psychotic Illness. The Canadian Journal of Psychiatry 2015;60(8):362-368. 110. Vanasse A, Courteau M, Ethier JF. The ’6W’ multidimensional model of care trajectories for patients with chronic ambulatory care sensitive conditions and hospital readmissions. Public Health Apr 2018;157:53-61. 111. Zhu Y, Li C, Huhn M, Rothe P, Krause M, Bighelli I, Schneider-Thoma J, Leucht S. How well do patients with a first episode of schizophrenia respond to antipsychotics: A systematic review and meta-analysis. European Neuropsychopharmacology 2017/09/01/ 2017;27(9):835-844. 112. Howes OD, McCutcheon R, Agid O, et al. Treatment-resistant schizophrenia: Treatment Response and Resistance in Psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. American Journal of Psychiatry 2016;174(3):216-229. 113. Andreasen NC, Carpenter Jr WT, Kane JM, Lasser RA, Marder SR, Weinberger DR. Remission in schizophrenia: proposed criteria and rationale for consensus. American Journal of Psychiatry 2005;162(3):441-449. 114. Cristarella T, Castillon G, Nepveu JF, Moride Y. Impact of schizophrenia relapse definition on the comparative effectiveness of oral versus injectable antipsychotics: A systematic review and meta-analysis of observational studies. Pharmacology Research & Perspectives Feb 2022;10(1). 115. Corrigan PW, Salzer M, Ralph RO, Sangster Y, Keck L. Examining the factor structure of the recovery assessment scale. Schizophrenia bulletin 2004;30(4):1035-1041. 116. Core Team Rv–. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 117. Muthén, Muthén. Mplus User Guide . 8 ed; 2017. 118. Percie du Sert O UJ. MplusLGM: Automate Latent Growth Mixture Modelling in ’Mplus’; 2025. 119. Hallquist MN, Wiley JF. MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Struct Equ Modeling 2018;25(4):621-638. 120. Nagin D. Group-based modeling of development : Harvard University Press; 2005. 121. Lennon H, Kelly S, Sperrin M, Buchan I, Cross AJ, Leitzmann M, Cook MB, Renehan AG. Framework to construct and interpret latent class trajectory modelling. BMJ Open 2018;8(7):e020683. 122. Van Der Nest G, Candel MJJM, Van Breukelen GJP, Passos VL. Multivariate versus univariate latent trajectory modelling: a comparison of class recovery performance and implications for multivariate model building strategies. Journal of Statistical Computation and Simulation 2025;95(8):1639-1665. 123. Muthén B. Statistical and substantive checking in growth mixture modeling: comment on Bauer and Curran (2003). 2003. 124. Muthén B, Asparouhov T. Growth mixture modeling with non-normal distributions. Statistics in Medicine 2015;34(6):1041-1058. 125. Vermunt JK. Latent class modeling with covariates: Two improved three-step approaches. Political analysis 2010;18(4):450-469. 126. Asparouhov T, Muthén B. Auxiliary variables in mixture modeling: Three-step approaches using M plus. Structural equation modeling: A multidisciplinary Journal 2014;21(3):329-341. 127. Wickrama KA, Lee TK, O’Neal CW, Lorenz FO. Higher-order growth curves and mixture modeling with Mplus: A practical guide : Routledge; 2021. 128. Santesteban-Echarri O, Paino M, Rice S, Gonzalez-Blanch C, McGorry P, Gleeson J, Alvarez-Jimenez M. Predictors of functional recovery in first-episode psychosis: A systematic review and meta-analysis of longitudinal studies. Clinical psychology review Dec 2017;58:59-75. 129. Dunkler D, Plischke M, Leffondré K, Heinze G. Augmented Backward Elimination: A Pragmatic and Purposeful Way to Develop Statistical Models. PloS one 2014;9(11):e113677. 130. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 2024:e078378. 131. Wang Y, Hsu H-Y, Kim E. Investigating the Impact of Covariate Inclusion on Sample Size Requirements of Factor Mixture Modeling: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal 2021;28(5):716-724. 132. Diallo TM, Morin AJ, Lu H. The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models. Psychological Methods 2017;22(1):166. 133. Kim S-Y. Sample Size Requirements in Single- and Multiphase Growth Mixture Models: A Monte Carlo Simulation Study. Structural Equation Modeling: A Multidisciplinary Journal 2012/07/20 2012;19(3):457-476. 134. Percie du Sert O, Unrau J, Dama M, et al. Latent Trajectories of Positive, Negative Symptoms and Functioning in Early Intervention Services for First-Episode Psychosis: A 2-Year Follow-Up Study. Schizophrenia Bulletin 2025. 135. Castor EDC. Castor Electronic Data Capture. Available at: . Accessed August 28, 2019. 136. Wilkinson MD, Dumontier M, Aalbersberg IJ, et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 2016;3(1):160018. 137. Bacellar IO, Morin G, Daniels S, Turecki G, Palaniyappan L, Lepage M. Opening up mental health research. Journal of Psychiatry and Neuroscience 2023;48(3):E209-E216. 138. Bergeron J, Doiron D, Marcon Y, Ferretti V, Fortier I. Fostering population-based cohort data discovery: The Maelstrom Research cataloguing toolkit. PloS one 2018;13(7):e0200926. 139. Zavaglia E, Joober R, Iyer SN, MacDonald K, Lepage M, Abadi S, Shah J, Ferrari M. Implementation of a digital measurement-based care approach in an early intervention service for psychosis: the PEPP-Montreal electronic data capture protocol and its alignment with learning health system principles. Schizophrenia Research 2025/08/01/ 2025;282:141-149. Information & Authors Information Version history V1 Version 1 24 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords clinical protocols long-term care longitudinal studies mental health recovery psychotic disorders Authors Affiliations Olivier Percie Du Sert 0000-0002-6283-2529 Douglas Research Centre View all articles by this author Joseph Ghanem Douglas Research Centre View all articles by this author Vanessa McGrory Douglas Research Centre View all articles by this author Karyne Anselmo Douglas Research Centre View all articles by this author Kelly Anderson 0000-0001-9843-404X Western University Department of Epidemiology and Biostatistics View all articles by this author Srividya Iyer 0000-0001-5367-9086 Douglas Research Centre View all articles by this author Ridha Joober Douglas Research Centre View all articles by this author Jai Shah Douglas Research Centre View all articles by this author Ashok Malla 0000-0002-5863-4191 Douglas Research Centre View all articles by this author Martin Lepage 0000-0003-4345-6502 [email protected] Douglas Research Centre View all articles by this author Metrics & Citations Metrics Article Usage 287 views 149 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Olivier Percie Du Sert, Joseph Ghanem, Vanessa McGrory, et al. Long-Term Trajectories of Multidimensional Outcomes in Psychosis Following Early Intervention During the Critical Period: The PEPP-Montreal 10+ Study Protocol. Authorea . 24 September 2025. DOI: https://doi.org/10.22541/au.175870089.97534629/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.175870089.97534629/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a004e43b8a6ddf88',t:'MTc3OTU0ODAyMQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();