The general movements assessment in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol.

The general movements assessment in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol. | 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 Systematic review update The general movements assessment in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol. Judy Seesahai, Maureen Luther, Carmen Cindy Rhoden, Paige Terrien Church, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.2.20965/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jul, 2020 Read the published version in Systematic Reviews → Version 3 posted 9 You are reading this latest preprint version Show more versions Abstract Background Prediction of long-term neurodevelopmental outcomes remains an elusive goal for neonatology. Clinical and socioeconomic markers have not proven to be adequately reliable. The limitation in prognostication includes those term and late-preterm infants born with neonatal encephalopathy. The General Movements Assessment tool by Prechtl has demonstrated reliability for identifying infants at risk for neuromotor impairment. This tool is non-invasive and cost-effective. The purpose of this study is to identify the published literature on how this tool applies to the prediction of cerebral palsy in term and late-preterm infants diagnosed with neonatal encephalopathy and so detect the research gaps. Methods We will conduct a systematic scoping review for data on sensitivity, specificity, positive and negative predictive value and describe the strengths and limitations of the results. This review will consider studies that included infants more than or equal to 34+0 weeks gestational age, diagnosed with neonatal encephalopathy, with a General Movements Assessment done between birth to six months of life and an assessment for cerebral palsy by at least two years of age. Experimental and quasi-experimental study designs including randomized controlled trials, non-randomized controlled trials, before and after studies, interrupted time-series studies and systematic reviews will be considered. Case reports, case series, case control and cross-sectional studies will be included. Text, opinion papers and animal studies will not be considered for inclusion in this scoping review as this is a highly specific and medical topic. Studies in the English language only will be considered. Studies published from at least 1970 will be included as this is around the time when the General Movements Assessment was first introduced in neonatology as a potential predictor of neuromotor outcomes. We will search five databases (MEDLINE, Embase, PsychINFO, Scopus and CINAHL). Two reviewers will conduct all screening and data extraction independently. The articles will be categorized according key findings and a critical appraisal performed. Discussion The results of this review will guide future research to improve early identification and timely intervention in infants with neonatal encephalopathy at risk of neuromotor impairment. Obstetrics & Gynecology Neonatal encephalopathy general movement assessment Prechtl hypoxia-ischemia encephalopathy cerebral palsy infants/neonates term babies preterm babies motor development. Background Prediction of long-term neurodevelopmental outcomes remains an elusive goal for neonatology. Clinical and socioeconomic outcome markers have not proven to be adequately reliable 1,2 . The limitation in prognostication includes those term and late-preterm infants born with neonatal encephalopathy (NE). NE describes those infants born with an atypical neurological exam and is by definition heterogeneous in etiology 3 . The specific etiology may not be clear for months to years later but the presentation is characterized by central nervous system disruption 4 and is associated with an increased risk for long-term neurodevelopmental challenges including cerebral palsy (CP). Infants presenting with NE are managed now with therapeutic hypothermia as the standard of care; this is presumptive management, and is time sensitive should the etiology be hypoxia/ischemia (Hypoxic Ischemic Encephalopathy (HIE)), in term and late-preterm infants 4,5 . Therapeutic hypothermia reduces the likelihood of challenging outcomes by containing any potential ongoing neurological injury. It does not, however, completely eradicate the possibility of long-term neurodevelopmental disability 6 . For parents of infants affected by NE, the desire for accurate prognostication is of tantamount importance 7 . This information can guide decisions around early intervention and, in severe cases, withdrawal of care for those infants with severe involvement. For those infants that survive NE and are at increased risk for CP, recent international recommendations now call for early detection and intervention of CP in order to improve functional outcomes 1,8,9 . These recommendations are based on mounting evidence for better detection tools as well as the benefits of early intervention. Historically, clinical and radiological predictors of neurological outcomes were used to classify the degree of NE. Severity scoring systems include the classical grading by Sarnat and Sarnat 10 in 1976, to the newer scores by Miller et al. 11 in 2004, with added parameters such as oral feeding difficulties and the presence of seizures. Radiologically, specific findings of diffusion restriction on magnetic resonance imaging (MRI) have been linked to later development of CP 4 . These predictors, however, were not sufficiently accurate 1,2 and the high costs of imaging as well as shortages in access further restricts the utility. Neurological examinations have historically been limited in predictive value but recent emerging evidence with an observational tool, the General Movements Assessment (GMA) developed by Dr. Heinz Prechtl has demonstrated strong predictive value 12, 13 . The GMA is a non-invasive, cost-effective tool with demonstrated reliability for identifying infants at risk for neuromotor impairment 14 . General movements (GMs) are complex, highly variable, whole-body movements which emerge in the fetus and progress through an age-specific developmental trajectory, dissipating by the end of the first four to five months of life 13 . Developmental progression and variety, or lack thereof, are indicators of nervous system integrity and can reflect neurodevelopmental outcomes 15 . Cramped synchronized (CS) and absent fidgety movements are considered abnormal GMAs, demonstrating developmental stereotypy 13 . Several researchers have looked at the GMA from different aspects. A preliminary search of PROSPERO, MEDLINE, the Cochrane Database of Systematic Reviews and the Joanna Briggs Institute (JBI) Database of Systematic Reviews and Implementation Reports was conducted to assess this research. There were two current systematic reviews on GMA, one in 2018 16 and the other in 2017 8 . In addition, eight older reviews were identified: seven systematic reviews 13,17-22 and one literature review 23 done between 2001 to 2013. The search also revealed three pending reviews identified around the topic of the predictive value of GMA 24-26 . These pending reviews were all systematic reviews. The key characteristics and main findings of the above reviews on GMA are presented in Table 1, Appendix I. In general, the latest systematic review, by Kwong et al. in 2018 16 , compared assessments of GMA and found that the Prechtl method had the best prediction of CP. In the 2017 systematic review by Novak et al. 8 , their group reviewed the evidence for the best tools for early, accurate diagnosis and intervention in infants at risk for CP. They considered all gestational ages (GA) and all diagnoses for infants that were high-risk. They recommended a combined approach for early CP diagnosis including history, neuroimaging, standardized neurological, and standardized motor assessments, to facilitate timely diagnosis and intervention. The other systematic reviews and literature review were all more than five years ago with the latest in 2013 13 . The findings of these older reviews are also summarized in Table 1. Similar to the latest two reviews, the older reviews either looked at preterms or all GA groups and diagnoses. Of the three pending systematic reviews identified in PROSPERO, the oldest review protocol (Kwong et al.) 26 was registered in 2016 by similar authors of the 2018 review mentioned above. The next review protocol was registered in February 2018 by Raghuram et al. 24 , and plans restrictions to preterms with all diagnoses, specifically examining automated movement recognition technology with the GMA. The third review protocol, registered in April 2018, by Angélica Valencia 25 is limited to preterm infants and is evaluating the type of method used for the recognition of the GMA, not the relationship of the GMA to neuromotor outcomes. None of these reviews specifically look at the population we identified for this scoping review, that is, term and late-preterm infants with NE. Thus, a gap exists in the literature to clearly identify the evidence for this specific population. The objective of this review is therefore, to identify the scope of the research with regards to the GMA and its ability to predict CP, in term and late-preterm infants with a diagnosis of NE, and to identify the gaps in the literature. Methods/design Review Question The primary research question for this review is: What is the published data on the predictive value of the GMA for the diagnosis of CP by two years of age in infants born at term or late-preterm presenting with NE? The secondary research question is: What is the gap in the literature when the GMA is used to predict CP by two years of age in infants born at term or late-preterm presenting with NE? Study Design A scoping method is chosen for this type of review as to fulfilling of the objective of the review it requires searching and assessing a wide range of research methodologies involving the use of the GMA in CP prediction. A scoping review will capture all types of relevant research on the topic in a systematic, transparent, rigorous and reproducible manner. This scoping review will be conducted in accordance with the JBI methodology for scoping reviews 27 . The objectives, inclusion criteria and methods for this scoping review are detailed in advance and documented in a proposal (included as Additional file 1). The title of our review was registered with JBI. Inherent in the nature of the scoping review is the inclusiveness of a wide range of literature, and so we anticipate differences in the data quality. Critical appraisal and data synthesis therefore will be challenging in terms of conclusive evidence as opposed to in a systematic review. The scoping review methodology is however especially advantageous to our question as these types of reviews target areas that have not been comprehensively assessed before. Eligibility Criteria The participant, concept, context (PCC) framework for scoping reviews will be used to define the review focus and can be found in Table 2. Table 2 Inclusion and exclusion criteria for the prediction of CP by the GMA in late-preterm and term infants with NE Inclusion criteria Exclusion criteria Participants Infants ≥ 34+0 weeks GA Diagnosis of NE GMA done between birth up to six months of life Assessment for CP by at least two years of age Infants born with: life threatening congenital abnormalities congenital viral infections an abnormal karyotype and metabolic disorders Concept GMA as a predictor of CP by two years of age is the main concept. Context Studies that reported on: - Infants with NE managed in hospitals and diagnosed by the standard of care (neurological history and examination) - Studies from all countries that have outcomes reported in the acute neonatal and in the follow-up period by two years of age - Studies in the English language only Note . CP = cerebral palsy, GA = gestational age, GMA = general movements assessment, NE = neonatal encephalopathy Participants This review will consider studies that include infants ≥ 34+0 weeks GA diagnosed with NE with a GMA done between birth to six months of life and an assessment for CP by at least two years of age (Table 2, Appendix II). Reviews with infants born with life threatening congenital abnormalities, congenital viral infections, an abnormal karyotype and metabolic disorders will be excluded. Those studies without a GMA or with any automated application of the GMA will also be excluded. Concept GMA as a predictor of CP by two years of age is the main concept. Studies that report on sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be considered for inclusion. Detailed definition of concepts can be found in Table 3. Context This review will consider studies that reported on infants with an existing diagnosis of NE managed in hospitals and diagnosed by the standard of care assessment of a neurological history and examination. Studies will be considered from all countries that have outcomes reported in the acute neonatal and in the follow-up period by two years of age. Studies in the English language only will be considered as there is no team member with adequate language skills to translate from any other language. Table 3 Definitions of concepts Concepts Definition Neonatal encephalopathy A clinically defined syndrome of disturbed neurologic function in the earliest days of life in an infant born at or beyond 35 weeks of gestation, manifested by a subnormal level of consciousness or seizures, and often accompanied by difficulty with initiating and maintaining respiration and depression of tone and reflexes 3 Late-preterm Neonates ≥ 34+0 to 36+6 weeks GA 28 Term Neonates 37+0 to 42+6 weeks GA 28 Cerebral palsy A group of permanent disorders of the development of movement and posture causing activity limitations that are attributed to non-progressive disturbances that occurred in the developing fetal or infant brain 29 General movements These are spontaneous movements present from early fetal life until about six months of life. GMs are variable, complex movements that occur frequently, lasting long enough to be observed. The whole body is involved in a variable sequence of limbs, neck, and trunk movements. Waxing and waning in intensity, force and speed, they have a gradual beginning and end. They involve rotations along the limb axis. Slight changes in direction are responsible for their fluid elegance. Impairment of the nervous system cause the loss of GMs complexity and variability resulting in monotonous and poor-quality movements. Specific abnormal GM patterns have been identified that reliably predict later cerebral palsy: 1) Cramped-synchronized GMs – a persistence of rigid movements that lack the normal fluidity. Contractions and relaxations occur almost concurrently in limb and trunk muscles. 2) The absence of fidgety GMs - fidgety movements are small movements of moderate speed with variable acceleration of neck, trunk, and limbs in all directions. Normally, they are the predominant movement pattern in an awake infant at 3 to 5 months 30 General movements assessment A comfortably dressed infant, preferably with bare arms and legs, is videoed in supine position. The duration of the video recording will depend on the age of the infant with premature infants requiring up to 30 to 60 minutes. Term age and older require 5 to 10 minutes of optimal recording. This recording does not require the observer’s presence. The trained observer reviews the recording later. The assessment is based on global visual Gestalt perception without acoustic signal to reduce distraction. Two to three recordings of the preterm, one recording at term or early post-term age or both, and at least one recording between 9- and 15-weeks’ post-term forms the basis of a developmental trajectory. An individual developmental trajectory indicates the consistency or inconsistency of normal or abnormal findings 30 Sensitivity The proportion of true positives that are correctly identified in a sample, or the true positive rate 31 Specificity The proportion of true negatives that are correctly identified in a sample, or the true negative rate 31 Positive predictive value The proportion of patients with positive test results who are correctly diagnosed 32 Negative predictive value The proportion of patients with negative test results who are correctly diagnosed 32 Note. GA = gestational age, GMs = general movements Search strategy A range of electronic databases will be searched to include medicine, nursing, allied health professions, sociology, psychology, education and social work. This scoping review will consider both experimental and quasi-experimental study designs including randomized controlled trials, non-randomized controlled trials, before and after studies and interrupted time-series studies. Case reports, case series, case control and cross-sectional studies will be included. In addition, systematic reviews that meet the inclusion criteria will be considered. Text and opinion papers will not be considered for inclusion in this scoping review as this is a highly specific and medical topic. Animal studies will not be included. Studies published from at least 1970 will be included as this is around the time when the GMA was first introduced in neonatology as a potential predictor of neuromotor outcomes 12 . The reference lists of articles will be scanned and experts in the infant developmental field will be consulted to identify studies relevant to our topic. The search strategy will be phased, firstly created in Ovid Medline using a combination of index terms and keywords around general movements, Prechtl, brain disease, HIE and perinatal asphyxia. An initial limited search of Ovid Medline, Embase and PsychINFO was undertaken to identify articles on the topic (See Additional file 2). There were no previous similar reviews. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles from this limited search will then be used to develop a more refined full search strategy in the second phase, for MEDLINE, Embase, PsychINFO, Scopus and CINAHL (Appendix III). The search strategy, including all identified keywords and index terms, will be adapted for each included information source. Study selection EndNote X9 will be used for citation collation. Duplicates will be removed manually. Covidence will be used for screening by two independent reviewers (JS and ML). Disagreements will be resolved through a third reviewer (RB). The results of the search will be reported in a Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews (PRISMA-ScR) flow diagram 33 . Data extraction, analysis and synthesis Publications meeting the inclusion criteria will have a full text review to validate their eligibility. Each article will be assessed independently by two authors (JS and RB). Extraction will be done after full text screening using a data extraction tool developed by the reviewers. Excluded studies closely meeting the inclusion criteria will be included in a separate table as they may contain many elements of our inclusion criteria but not present separately the specific criteria of our interest. Further investigation of their data may provide significant results. Authors will be contacted to access further information and reassess eligibility of these studies. Excluded studies will be documented with reasons for their exclusion. The data extracted from the identified studies will include specific details about the population, concept and context. Two tables will be generated with the first table having information on the key characteristics of each study, including author, year of publication, geographical setting, type of study, demographics of the participants, period over which the study was conducted, the method of identification of neonates at high-risk, if therapeutic hypothermia was instituted as management for NE, type of spontaneous movement assessment used, age at which participants were assessed, the age at which CP was diagnosed and the methods used for neurological examination in the studies. The second table will have information on the key findings, the predictive indices used for the GMA in relation to CP (sensitivity, specificity, PPV and NPV), limitations of the studies and where relevant, reasons for exclusion in the studies that met most but not all of the inclusion criteria. These lists will be iterative. As the process evolves, the data extraction form may require modification to ensure all relevant information is included. Additionally, even though this was a scoping review and does not require a critical appraisal, the critical appraisal tool for JBI 34 will helped to identify differences and similarities between the included studies. The answers to the JBI critical appraisal tool will be detailed in a table. Discussion The extracted data will be presented in diagrammatic or tabular form in a manner that aligns with the objective of this scoping review. A narrative summary will accompany the tabulated and/or charted results and will describe how the results relate to the reviews objective and question. The critical appraisal result will also be tabulated and this will be used to further identify the strengths and limitations of the studies as well as the key findings in relationship to the objective of this scoping review. The strengths and limitations of our scoping review method on the credibility of the results will be detailed. The discussion and conclusions will reflect on the implications for future research and patient management. Protocol amendments Important amendments to the protocol will be reported with the results of the review. What this study will add This study will examine the scope of the literature with respect to the use of the GMA in NE for the prediction of CP. Assessment of the extent of the knowledge on this topic seems to have not previously been done. By inclusion of a critical appraisal of the available relevant literature, it will facilitate an appreciation of the quality of the existing knowledge in this area. It will therefore identify gaps in the research especially in the setting of NE management with therapeutic hypothermia. List Of Abbreviations CP cerebral palsy; CS, cramped synchronized; GMs, general movements; GA, gestational age; GMA, general movements assessment; HIE, hypoxic ischemic encephalopathy; JBI, Joanna Briggs Institute; MRI, magnetic resonance imaging; NE, Neonatal encephalopathy; NPV, negative predictive value; PCC, participant, concept, context; PPV, positive predictive value; PRISMA-ScR, Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review. Declarations Ethics approval and consent to participate Ethical approval will not be required as this is a scoping review of the literature and will not contain information directly identifying patients or content requiring patient consent. Consent for publication Not applicable Availability of data and materials Data sharing is not applicable to this article as no datasets were generated or analysed during the current study. Materials during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding There is no funding required for this review. Authors' contributions First author: Judy Seesahai Contributions: Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper. Second author: Maureen Luther Contributions : Contribution to acquisition, analysis and interpretation of data as well as well as involved in revisions to the paper. Paige Terrien Church Contributions : Substantial contributions to research design, analysis and interpretation of data as well as drafting the paper. Carmen Cindy Rhoden Contributions : Initial data search and drafting of paper. Elizabeth Azstalos Contributions : Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper. Supervisor: Thomas Rotter Contributions : Substantial contributions to research design and reviewing of the paper. Principal Investigator: Rudaina Banihani Contributions : Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper. Acknowledgements This review will contribute to a Master in Healthcare Quality degree for JS. The authors would also like to acknowledge the librarians that assisted with this research project, namely from the Sunnybrook R. Ian MacDonald Library, Henry Lam and Reena Besa, as well as the librarians Paola Durando and Sandra McKeown of the Bracken Health Sciences Library, Queen’s University. References Finer NN, Robertson CM, Richards RT, Pinnell LE, Peters KL. Hypoxic-ischemic encephalopathy in term neonates: perinatal factors and outcome. J Pediatr. 1981 Jan; 98 (1):112-7. DOI: 10.1016/s0022-3476(81)80555-0 Campbell EE, Gilliland J, Dworatzek PDN, De Vrijer B, Penava D, Seabrook JA. Socioeconomic status and adverse birth outcomes: A population-based Canadian sample. Journal of Biosocial Science. Cambridge University Press; 2018;50(1):102–13. (2014). Executive Summary: Neonatal Encephalopathy and Neurologic Outcome, Second Edition. Obstetrics & Gynecology, 123(4), 896–901. doi: 10.1097/01.AOG.0000445580.65983.d2. Glass HC. 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Available from https://reviewersmanual.joannabriggs.org/ Supplementary Files Additionalfile2.docx Additionalfile1.docx PRISMAScRGMAandNE.docx Cite Share Download PDF Status: Published Journal Publication published 04 Jul, 2020 Read the published version in Systematic Reviews → Version 3 posted Editorial decision: Accept 10 Apr, 2020 Review # 2 received at journal 07 Apr, 2020 Reviewer # 2 agreed at journal 31 Mar, 2020 Review # 1 received at journal 27 Mar, 2020 Editor assigned by journal 26 Mar, 2020 Reviewers invited by journal 26 Mar, 2020 Reviewer # 1 agreed at journal 26 Mar, 2020 Submission checks completed at journal 25 Mar, 2020 Editor invited by journal 25 Mar, 2020 You are reading this latest preprint version Show more versions 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. 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15:46:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":164258,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-11563/v3/Additional file 1.docx"},{"id":766698,"identity":"70f103b5-0524-44b9-a69e-a6dfe9355f2f","added_by":"auto","created_at":"2020-03-30 15:46:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":108045,"visible":true,"origin":"","legend":"","description":"","filename":"PRISMAScRGMAandNE.docx","url":"https://assets-eu.researchsquare.com/files/rs-11563/v3/PRISMA-ScR GMA and NE.docx"}],"financialInterests":"","formattedTitle":"The general movements assessment in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol.","fulltext":[{"header":"Background","content":"\u003cp\u003ePrediction of long-term neurodevelopmental outcomes remains an elusive goal for neonatology. Clinical and socioeconomic outcome markers have not proven to be adequately reliable\u003csup\u003e1,2\u003c/sup\u003e. The limitation in prognostication includes those term and late-preterm infants born with neonatal encephalopathy (NE).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNE describes those infants born with an atypical neurological exam and is by definition heterogeneous in etiology\u003csup\u003e3\u003c/sup\u003e.\u0026nbsp; The specific etiology may not be clear for months to years later but the presentation is characterized by central nervous system disruption\u003csup\u003e4\u003c/sup\u003e and is associated with an increased risk for long-term neurodevelopmental challenges including cerebral palsy (CP).\u0026nbsp; Infants presenting with NE are managed now with therapeutic hypothermia as the standard of care; this is presumptive management, and is time sensitive should the etiology be hypoxia/ischemia (Hypoxic Ischemic Encephalopathy (HIE)), in term and late-preterm infants\u003csup\u003e4,5\u003c/sup\u003e. Therapeutic hypothermia reduces the likelihood of challenging outcomes by containing any potential ongoing neurological injury.\u0026nbsp; It does not, however, completely eradicate the possibility of long-term neurodevelopmental disability\u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFor parents of infants affected by NE, the desire for accurate prognostication is of tantamount importance\u003csup\u003e7\u003c/sup\u003e. This information can guide decisions around early intervention and, in severe cases, withdrawal of care for those infants with severe involvement.\u0026nbsp; For those infants that survive NE and are at increased risk for CP, recent international recommendations now call for early detection and intervention of CP in order to improve functional outcomes\u003csup\u003e1,8,9\u003c/sup\u003e. These recommendations are based on mounting evidence for better detection tools as well as the benefits of early intervention.\u003c/p\u003e\n\u003cp\u003eHistorically, clinical and radiological predictors of neurological outcomes were used to classify the degree of NE. Severity scoring systems include the classical grading by Sarnat and Sarnat\u003csup\u003e10\u003c/sup\u003e in 1976, to the newer scores by Miller et al.\u003csup\u003e11\u003c/sup\u003e in 2004, with added parameters such as oral feeding difficulties and the presence of seizures. Radiologically, specific findings of diffusion restriction on magnetic resonance imaging (MRI) have been linked to later development of CP\u003csup\u003e4\u003c/sup\u003e. These predictors, however, were not sufficiently accurate\u003csup\u003e1,2 \u003c/sup\u003eand the high costs of imaging as well as shortages in access further restricts the utility.\u0026nbsp; Neurological examinations have historically been limited in predictive value but recent emerging evidence with an observational tool, the General Movements Assessment (GMA) developed by Dr. Heinz Prechtl has demonstrated strong predictive value\u003csup\u003e12, 13\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe GMA is a non-invasive, cost-effective tool with demonstrated reliability for identifying infants at risk for neuromotor impairment\u003csup\u003e14\u003c/sup\u003e. General movements (GMs) are complex, highly variable, whole-body movements which emerge in the fetus and progress through an age-specific developmental trajectory, dissipating by the end of the first four to five months of life\u003csup\u003e13\u003c/sup\u003e. Developmental progression and variety, or lack thereof, are indicators of nervous system integrity and can reflect neurodevelopmental outcomes\u003csup\u003e15\u003c/sup\u003e. Cramped synchronized (CS) and absent fidgety movements are considered abnormal GMAs, demonstrating developmental stereotypy\u003csup\u003e13\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSeveral researchers have looked at the GMA from different aspects. A preliminary search of PROSPERO, MEDLINE, the Cochrane Database of Systematic Reviews and the Joanna Briggs Institute (JBI) Database of Systematic Reviews and Implementation Reports was conducted to assess this research. There were two current systematic reviews on GMA, one in 2018\u003csup\u003e16\u003c/sup\u003e and the other in 2017\u003csup\u003e8\u003c/sup\u003e. In addition, eight older reviews were identified: seven systematic reviews\u003csup\u003e13,17-22 \u003c/sup\u003eand one literature review\u003csup\u003e23\u003c/sup\u003e done between 2001 to 2013. The search also revealed three pending reviews identified around the topic of the predictive value of GMA\u003csup\u003e24-26\u003c/sup\u003e. These pending reviews were all systematic reviews.\u003c/p\u003e\n\u003cp\u003eThe key characteristics and main findings of the above reviews on GMA are presented in Table 1, Appendix I. In general, the latest systematic review, by Kwong et al. in 2018\u003csup\u003e16\u003c/sup\u003e, compared assessments of GMA and found that the Prechtl method had the best prediction of CP. In the 2017 systematic review by Novak et al.\u003csup\u003e8\u003c/sup\u003e, their group reviewed the evidence for the best tools for early, accurate diagnosis and intervention in infants at risk for CP. They considered all gestational ages (GA) and all diagnoses for infants that were high-risk. They recommended a combined approach for early CP diagnosis including history, neuroimaging, standardized neurological, and standardized motor assessments, to facilitate timely diagnosis and intervention. The other systematic reviews and literature review were all more than five years ago with the latest in 2013\u003csup\u003e13\u003c/sup\u003e. The findings of these older reviews are also summarized in Table 1. Similar to the latest two reviews, the older reviews either looked at preterms or all GA groups and diagnoses.\u003c/p\u003e\n\u003cp\u003eOf the three pending systematic reviews identified in PROSPERO, the oldest review protocol (Kwong et al.)\u003csup\u003e26\u003c/sup\u003e was registered in 2016 by similar authors of the 2018 review mentioned above. The next review protocol was registered in February 2018 by Raghuram et al.\u003csup\u003e24\u003c/sup\u003e, and plans restrictions to preterms with all diagnoses, specifically examining automated movement recognition technology with the GMA. The third review protocol, registered in April 2018, by Ang\u0026eacute;lica Valencia\u003csup\u003e25 \u003c/sup\u003eis limited to preterm infants and is evaluating the type of method used for the recognition of the GMA, not the relationship of the GMA to neuromotor outcomes. None of these reviews specifically look at the population we identified for this scoping review, that is, term and late-preterm infants with NE. Thus, a gap exists in the literature to clearly identify the evidence for this specific population.\u003c/p\u003e\n\u003cp\u003eThe objective of this review is therefore, to identify the scope of the research with regards to the GMA and its ability to predict CP, in term and late-preterm infants with a diagnosis of NE, and to identify the gaps in the literature.\u003c/p\u003e\n\u003cp\u003e\n \u003cbr\u003e\n \u003cimg 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\"\u003e\n\u003c/p\u003e\n\u003cp\u003e\n 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\"\u003e\n 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\"\u003e\n\u003c/p\u003e"},{"header":"Methods/design","content":"\u003cp\u003e\u003cstrong\u003eReview Question\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary research question for this review is: What is the published data on the predictive value of the GMA for the diagnosis of CP by two years of age in infants born at term or late-preterm presenting with NE? \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe secondary research question is: What is the gap in the literature when the GMA is used to predict CP by two years of age in infants born at term or late-preterm presenting with\u0026nbsp; NE?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA scoping method is chosen for this type of review as to fulfilling of the objective of the review it requires searching and assessing a wide range of research methodologies involving the use of the GMA in CP prediction. A scoping review will capture all types of relevant research on the topic in a systematic, transparent, rigorous and reproducible manner. This scoping review will be conducted in accordance with the JBI methodology for scoping reviews\u003csup\u003e27\u003c/sup\u003e. The objectives, inclusion criteria and methods for this scoping review are detailed in advance and documented in a proposal (included as Additional file 1). The title of our review was registered with JBI.\u003c/p\u003e\n\u003cp\u003eInherent in the nature of the scoping review is the inclusiveness of a wide range of literature, and so we anticipate differences in the data quality. Critical appraisal and data synthesis therefore will be challenging in terms of conclusive evidence as opposed to in a systematic review. The scoping review methodology is however especially advantageous to our question as these types of reviews target areas that have not been comprehensively assessed before.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe participant, concept, context (PCC) framework for scoping reviews will be used to define the review focus and can be found in Table 2.\u003c/p\u003e\n\u003ctable style=\"border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 467.5pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: 12px; font-family: Helvetica;\"\u003eTable 2\u003c/span\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eInclusion and exclusion criteria for the prediction of CP by the GMA in late-preterm and term infants with NE\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eInclusion criteria\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eExclusion criteria\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eParticipants\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184.5pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eInfants ≥ 34+0 weeks GA\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eDiagnosis of NE\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eGMA done between birth up to six months of life\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eAssessment for CP by at least two years of age\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eInfants born with:\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cdiv style=\"margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\n \u003cul style=\"margin-bottom:0in;list-style-type: undefined;margin-left:0in;\"\u003e\n \u003cli style=\"margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003elife threatening congenital abnormalities\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli style=\"margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003econgenital viral infections\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli style=\"margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003ean abnormal karyotype and\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli style=\"margin-top:0in;margin-right:0in;margin-bottom:8.0pt;margin-left:0in;line-height:107%;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003emetabolic disorders\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/div\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:.5in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103.25pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eConcept\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184.5pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"color: black;\"\u003eGMA as a predictor of CP by two years of age is the main concept.\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 103.25pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eContext\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 184.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003eStudies that reported on:\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e- Infants with NE managed in hospitals and diagnosed by the standard of care (neurological history and examination)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e- Studies from all countries that have outcomes reported in the acute neonatal and in the follow-up period by two years of age\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e- Studies in the English language only\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 179.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 467.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:2.0pt;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: 12px; font-family: Helvetica;\"\u003e\u003cem\u003eNote\u003c/em\u003e. CP = cerebral palsy, GA = gestational age, GMA = general movements assessment, NE = neonatal encephalopathy\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cbr\u003e\n\u003cp\u003e\u003cem\u003eParticipants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis review will consider studies that include infants \u0026ge; 34+0 weeks GA diagnosed with NE with a GMA done between birth to six months of life and an assessment for CP by at least two years of age (Table 2, Appendix II).\u003c/p\u003e\n\u003cp\u003eReviews with infants born with life threatening congenital abnormalities, congenital viral infections, an abnormal karyotype and metabolic disorders will be excluded. Those studies without a GMA or with any automated application of the GMA will also be excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConcept\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGMA as a predictor of CP by two years of age is the main concept. Studies that report on sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be considered for inclusion. Detailed definition of concepts can be found in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eContext\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis review will consider studies that reported on infants with an existing diagnosis of NE managed in hospitals and diagnosed by the standard of care assessment of a neurological history and examination. Studies will be considered from all countries that have outcomes reported in the acute neonatal and in the follow-up period by two years of age. Studies in the English language only will be considered as there is no team member with adequate language skills to translate from any other language.\u003c/p\u003e\n\u003ctable style=\"border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 467.5pt;border-top: 1pt solid windowtext;border-left: none;border-bottom: 1pt solid windowtext;border-right: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: 12px; font-family: Helvetica;\"\u003eTable 3\u003c/span\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eDefinitions of concepts\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;text-align:center;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eConcepts\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height:normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;text-align:center;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eDefinition\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eNeonatal encephalopathy\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eA clinically defined syndrome of disturbed neurologic function in the earliest days of life in an infant born at or beyond 35 weeks of gestation, manifested by a subnormal level of consciousness or seizures, and often accompanied by difficulty with initiating and maintaining respiration and depression of tone and reflexes\u003csup\u003e3\u003c/sup\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eLate-preterm\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eNeonates ≥ 34+0 to 36+6 weeks GA\u003csup\u003e28\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eTerm\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eNeonates 37+0 to 42+6 weeks GA\u003csup\u003e28\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eCerebral palsy\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eA group of permanent disorders of the development of movement and posture causing activity limitations that are attributed to non-progressive disturbances that occurred in the developing fetal or infant brain\u003csup\u003e29\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eGeneral movements\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eThese are spontaneous movements present from early fetal life until about six months of life. GMs are variable, complex movements that occur frequently, lasting long enough to be observed. The whole body is involved in a variable sequence of limbs, neck, and trunk movements. Waxing and waning in\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eintensity, force and speed, they have a gradual beginning and end. They involve rotations along the limb axis. Slight changes in direction are responsible for their fluid elegance. Impairment of the nervous system cause the loss of GMs complexity and variability resulting in monotonous and poor-quality movements.\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eSpecific abnormal GM patterns have been identified that reliably predict later cerebral palsy:\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e1) Cramped-synchronized GMs – a persistence of rigid movements that lack the normal fluidity. Contractions and relaxations occur almost concurrently in limb and trunk\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003emuscles.\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e2) The absence of fidgety GMs - fidgety movements are small movements of moderate speed with variable acceleration of neck, trunk, and limbs in all directions. Normally, they are the predominant movement pattern in an\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eawake infant at 3 to 5 months\u003csup\u003e30\u003c/sup\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eGeneral movements assessment\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eA comfortably dressed infant, preferably with bare arms and legs, is videoed in supine position. The duration of the video recording will depend on the age of the infant with premature infants requiring up to 30 to 60 minutes. Term age and older require 5 to 10 minutes of optimal recording. This recording does not require the observer’s presence. The trained observer reviews the recording later. The assessment is based on global visual Gestalt perception without acoustic signal to reduce distraction. Two to three recordings of the preterm, one recording at term or early post-term age or both, and at least one recording between 9- and 15-weeks’ post-term forms the basis of a developmental trajectory. An individual developmental trajectory indicates the consistency or inconsistency of normal or abnormal findings\u003csup\u003e30\u003c/sup\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eSensitivity\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eThe proportion of true positives that are correctly identified in a sample, or the true positive rate\u003csup\u003e31\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eSpecificity\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eThe proportion of true negatives that are correctly identified in a sample, or the true negative rate\u003csup\u003e31\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003ePositive predictive value\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border: none;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eThe proportion of patients with positive test results who are correctly diagnosed\u003csup\u003e32\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eNegative predictive value\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 305.75pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003eThe proportion of patients with negative test results who are correctly diagnosed\u003csup\u003e32\u003c/sup\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-size: \n 12px;\"\u003e\u003cspan style=\"font-family: \n Helvetica;\"\u003e\u0026nbsp;\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 467.5pt;border-top: none;border-right: none;border-left: none;border-image: initial;border-bottom: 1pt solid windowtext;padding: 0in 5.4pt;vertical-align: top;\"\u003e\n \u003cp style=\"margin-top:0in;margin-right:0in;margin-bottom:.0001pt;margin-left:0in;line-height: normal;font-size:15px;font-family:\u0026quot;Calibri\u0026quot;,sans-serif;\"\u003e\u003cspan style=\"font-family: Helvetica; font-size: 12px;\"\u003e\u003cem\u003eNote.\u003c/em\u003e GA = gestational age, GMs = general movements\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cbr\u003e\u003cbr\u003e\n\u003cp\u003e\u003cstrong\u003eSearch strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA range of electronic databases will be searched to include medicine, nursing, allied health professions, sociology, psychology, education and social work. This scoping review will consider both experimental and quasi-experimental study designs including randomized controlled trials, non-randomized controlled trials, before and after studies and interrupted time-series studies. Case reports, case series, case control and cross-sectional studies will be included. In addition, systematic reviews that meet the inclusion criteria will be considered. Text and opinion papers will not be considered for inclusion in this scoping review as this is a highly specific and medical topic. Animal studies will not be included. Studies published from at least 1970 will be included as this is around the time when the GMA was first introduced in neonatology as a potential predictor of neuromotor outcomes\u003csup\u003e12\u003c/sup\u003e. The reference lists of articles will be scanned and experts in the infant developmental field will be consulted to identify studies relevant to our topic.\u003c/p\u003e\n\u003cp\u003eThe search strategy will be phased, firstly created in Ovid Medline using a combination of index terms and keywords around general movements, Prechtl, brain disease, HIE and perinatal asphyxia. An initial limited search of Ovid Medline, Embase and PsychINFO was undertaken to identify articles on the topic (See Additional file 2). There were no previous similar reviews. The text words contained in the titles and abstracts of relevant articles, and the index terms used to describe the articles from this limited search will then be used to develop a more refined full search strategy in the second phase, for MEDLINE, Embase, PsychINFO, Scopus and CINAHL (Appendix III). The search strategy, including all identified keywords and index terms, will be adapted for each included information source.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndNote\u0026nbsp;X9 will be used for citation collation. Duplicates will be removed manually. Covidence will be used for screening by two independent reviewers (JS and ML). Disagreements will be resolved through a third reviewer (RB). The results of the search will be reported in a Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews (PRISMA-ScR) flow diagram\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction, analysis and synthesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublications meeting the inclusion criteria will have a full text review to validate their eligibility. Each article will be assessed independently by two authors (JS and RB). Extraction will be done after full text screening using a data extraction tool developed by the reviewers. Excluded studies closely meeting the inclusion criteria will be included in a separate table as they may contain many elements of our inclusion criteria but not present separately the specific criteria of our interest. Further investigation of their data may provide significant results. Authors will be contacted to access further information and reassess eligibility of these studies. Excluded studies will be documented with reasons for their exclusion.\u003c/p\u003e\n\u003cp\u003eThe data extracted from the identified studies will include specific details about the population, concept and context. Two tables will be generated with the first table having information on the key characteristics of each study, including author, year of publication, geographical setting, type of study, demographics of the participants, period over which the study was conducted, the method of identification of neonates at high-risk, if therapeutic hypothermia was instituted as management for NE, type of spontaneous movement assessment used, age at which participants were assessed, the age at which CP was diagnosed and the methods used for neurological examination in the studies. The second table will have information on the key findings, the predictive indices used for the GMA in relation to CP (sensitivity, specificity, PPV and NPV), limitations of the studies and where relevant, reasons for exclusion in the studies that met most but not all of the inclusion criteria. These lists will be iterative. As the process evolves, the data extraction form may require modification to ensure all relevant information is included. Additionally, even though this was a scoping review and does not require a critical appraisal, the critical appraisal tool for JBI\u003csup\u003e34 \u003c/sup\u003ewill helped to identify differences and similarities between the included studies. The answers to the JBI critical appraisal tool will be detailed in a table.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe extracted data will be presented in diagrammatic or tabular form in a manner that aligns with the objective of this scoping review. A narrative summary will accompany the tabulated and/or charted results and will describe how the results relate to the reviews objective and question. The critical appraisal result will also be tabulated and this will be used to further identify the strengths and limitations of the studies as well as the key findings in relationship to the objective of this scoping review. The strengths and limitations of our scoping review method on the credibility of the results will be detailed. The discussion and conclusions will reflect on the implications for future research and patient management.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtocol amendments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImportant amendments to the protocol will be reported with the results of the review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study will add\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study will examine the scope of the literature with respect to the use of the GMA in NE for the prediction of CP. Assessment of the extent of the knowledge on this topic seems to have not previously been done. By inclusion of a critical appraisal of the available relevant literature, it will facilitate an appreciation of the quality of the existing knowledge in this area. It will therefore identify gaps in the research especially in the setting of NE management with therapeutic hypothermia.\u003c/p\u003e"},{"header":"List Of Abbreviations","content":"\u003cp\u003eCP cerebral palsy; CS, cramped synchronized; GMs, general movements; GA, gestational age; GMA, general movements assessment; HIE, hypoxic ischemic encephalopathy; JBI, Joanna Briggs Institute; MRI, magnetic resonance imaging; NE, Neonatal encephalopathy; NPV, negative predictive value; PCC, participant, concept, context; PPV, positive predictive value; PRISMA-ScR, Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping review.\u003c/p\u003e"},{"header":"Declarations ","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval will not be required as this is a scoping review of the literature and will not contain information directly identifying patients or content requiring patient consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData sharing is not applicable to this article as no datasets were generated or analysed during the current study. Materials during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding required for this review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst author: Judy Seesahai\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions:\u003c/strong\u003e Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecond author: Maureen Luther\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Contribution to acquisition, analysis and interpretation of data as well as well as involved in revisions to the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePaige Terrien Church\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Substantial contributions to research design, analysis and interpretation of data as well as drafting the paper.\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarmen Cindy Rhoden\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Initial data search and drafting of paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eElizabeth Azstalos\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupervisor: Thomas Rotter\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Substantial contributions to research design and reviewing of the paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal Investigator: Rudaina Banihani\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e: Substantial contributions to research design, acquisition, analysis and interpretation of data as well as drafting the paper. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis review will contribute to a Master in Healthcare Quality degree for JS. The authors would also like to acknowledge the librarians that assisted with this research project, namely from the Sunnybrook R. Ian MacDonald Library, Henry Lam and Reena Besa, as well as the librarians Paola Durando and Sandra McKeown of the Bracken Health Sciences Library, Queen\u0026rsquo;s University.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFiner NN, Robertson CM, Richards RT, Pinnell LE, Peters KL. Hypoxic-ischemic encephalopathy in term neonates: perinatal factors and outcome. J Pediatr. 1981 Jan; 98 (1):112-7. DOI: 10.1016/s0022-3476(81)80555-0\u003c/li\u003e\n\u003cli\u003eCampbell EE, Gilliland J, Dworatzek PDN, De Vrijer B, Penava D, Seabrook JA. Socioeconomic status and adverse birth outcomes: A population-based Canadian sample. Journal of Biosocial Science. Cambridge University Press; 2018;50(1):102\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003e(2014). Executive Summary: Neonatal Encephalopathy and Neurologic Outcome, Second Edition. Obstetrics \u0026amp; Gynecology, 123(4), 896\u0026ndash;901. doi: 10.1097/01.AOG.0000445580.65983.d2.\u003c/li\u003e\n\u003cli\u003eGlass HC. Hypoxic-ischemic encephalopathy and other neonatal encephalopathies. [Review]. Continuum (Minneap Minn). 2018 Feb;57\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eAmerican Academy of Pediatrics, Committee on Fetus and Newborn. Hypothermia and neonatal encephalopathy. Pediatrics. 2014 Jun;133(6):1146\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eStaub K, Baardsnes J, H\u0026eacute;bert N, H\u0026eacute;bert M, Newell S, Pearce R. Our child is not just a gestational age. A first‐hand account of what parents want and need to know before premature birth. Acta Paediatr. 2014;103(10):1035\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eBanihani R TCP. Neonatal Encephalopathy. In: Needelman H JB, editor. Follow-Up for NICU Graduates. 2018. p. 155\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eShepherd E, Salam RA, Middleton P, Han S, Makrides M, McIntyre S, et al. Neonatal interventions for preventing cerebral palsy: an overview of Cochrane Systematic Reviews. Cochrane Database of Systematic Reviews 2018, Issue 6. Art. No.: CD012409. DOI: 10.1002/14651858.CD012409.pub2.\u003c/li\u003e\n\u003cli\u003eNovak I, Morgan C, Adde L, et al. Early, accurate diagnosis and early intervention in cerebral palsy: advances in diagnosis and treatment. JAMA Pediatr. 2017;171(9):897\u0026ndash;907. doi:10.1001/jamapediatrics.2017.1689.\u003c/li\u003e\n\u003cli\u003eSarnat H, Sarnat M. Neonatal encephalopathy following fetal distress. Arch Neurol.1976.33:695-705.\u003c/li\u003e\n\u003cli\u003eMiller SP, Latal B, Clark H, Barnwell A, Glidden D, Barkovich AJ, et al. Clinical signs predict 30-month neurodevelopmental outcome after neonatal encephalopathy. American Journal of Obstetrics and Gynecology. 2004.190(1):93-99. DOI: \u003ca href=\"https://doi.org/10.1016/S0002-9378(03)00908-6\"\u003ehttps://doi.org/10.1016/S0002-9378(03)00908-6\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eEinspieler C, Prechtl HFR, Bos AF, Ferrari F, Cioni G. In: Hart HM, Pountney M, Pearsall S (Editors). Developmental Medicine No. 167. Prechtl\u0026rsquo;s method on the qualitative assessment of general movements in preterm, term and young infants. 1st ed. Mac Keith Press c2004. p ix - xi.\u003c/li\u003e\n\u003cli\u003eBosanquet M, Copeland L, Ware R, Boyd R. A systematic review of tests to predict cerebral palsy in young children. Dev Med Child Neurol. 2013;55:418\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eHeineman KR1, Hadders-Algra M. Evaluation of neuromotor function in infancy-A systematic review of available methods. J Dev Behav Pediatr. 2008: Aug 29(4):315-23. doi: 10.1097/DBP.0b013e318182a4ea.\u003c/li\u003e\n\u003cli\u003eHadders-Algra M. General Movements: A Window for early identification of children at high risk for developmental disorders. J Pediatr. 2004 May 11;145(2 Supplement):S12\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eKwong AKL, Fitzgerald TL, Doyle LW, Cheong JL, Spittle AJ. Predictive validity of spontaneous early infant movement for later cerebral palsy: a systematic review. Dev Med Child Neurol. 2018 Feb 22;60(5):480-489.\u003c/li\u003e\n\u003cli\u003eNoble Y \u0026amp; Boyd R. Neonatal assessments for the preterm infant up to 4 months corrected age: a systematic review. Dev Med Child Neurol. 2012 Nov, 54: 129\u0026ndash;39.\u003c/li\u003e\n\u003cli\u003eZuk L. Fetal and infant spontaneous general movements as predictors of developmental disabilities. Dev Disabil Res Rev.2011;17:93\u0026ndash;101. Available from: \u003ca href=\"https://proxy.queensu.ca/login?url=http://ovidsp.ovid.com?T=JS\u0026amp;CSC=Y\u0026amp;NEWS=N\u0026amp;PAGE=fulltext\u0026amp;D=med7\u0026amp;AN=23362029\"\u003ehttps://proxy.queensu.ca/login?url=http://ovidsp.ovid.com?T=JS\u0026amp;CSC=Y\u0026amp;NEWS=N\u0026amp;PAGE=fulltext\u0026amp;D=med7\u0026amp;AN=23362029\u003c/a\u003e \u003ca href=\"https://onlinelibrary.wiley.com/doi/abs/10.1002/ddrr.1104\"\u003ehttps://onlinelibrary.wiley.com/doi/abs/10.1002/ddrr.1104\u003c/a\u003e \u003ca href=\"https://dx.doi.org/10.1002/ddrr.1104\"\u003ehttps://dx.doi.org/10.1002/ddrr.1104\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eDarsaklis V, Snider LM, Majnemer A, Mazer B. Predictive validity of Prechtl\u0026rsquo;s method on the qualitative assessment of general movements: a systematic review of the evidence. Dev Med Child Neurol. 2011 Jun 17;53(10):896\u0026ndash;906.\u003c/li\u003e\n\u003cli\u003eBurger M, Louw QA. The predictive validity of general movements \u0026ndash; a systematic review. Eur J Paediatr Neurol. 2009;13(5):408\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eSpittle AJ, Doyle LW, Boyd RN. A systematic review of the clinimetric properties of neuromotor assessments for preterm infants during the first year of life. Dev Med Child Neurol. 2008 Apr 8;50(4):254\u0026ndash;66.\u003c/li\u003e\n\u003cli\u003e\u003cbr /\u003e Hadders-Algra M. Evaluation of motor function in young infants by means of the assessment of general movements: a review. Pediatr Phys Ther. 2001 Apr 1;13(1):27\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eSantos RS., Ara\u0026uacute;jo APQC., Porto MAS. Early diagnosis of abnormal development of preterm newborns: assessment instruments. J. Pediatr. (Rio J.) [Internet]. 2008 Aug [cited 2019 Aug 07];84(4):289-299. Available from: \u003ca href=\"http://www.scielo.br.proxy.queensu.ca/scielo.php?script=sci_arttext\u0026amp;pid=S0021-75572008000400003\u0026amp;lng=en\"\u003ehttp://www.scielo.br.proxy.queensu.ca/scielo.php?script=sci_arttext\u0026amp;pid=S0021-75572008000400003\u0026amp;lng=en\u003c/a\u003e. \u003ca href=\"http://dx.doi.org.proxy.queensu.ca/10.1590/S0021-75572008000400003\"\u003ehttp://dx.doi.org.proxy.queensu.ca/10.1590/S0021-75572008000400003\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eRaghuram K, Orlandi S, Church P, Chau T, Uleryk E, Pechlivanoglou P, et al. Can an automated general movements assessment be used to predict motor impairment in high-risk infants? A systematic review and meta-analysis of diagnostic accuracy. PROSPERO International prospective register of systematic reviews. 2018 Apr 16; Available from: \u003ca href=\"http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018087892\"\u003ehttp://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018087892\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eValencia A. Discriminative and predictive validity of the general movements assessment: a systematic review. PROSPERO International prospective register of systematic reviews. 2018 Feb 14;Available from: \u003ca href=\"http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018088724\"\u003ehttp://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018088724\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eKwong AKL, Fitzgerald TL, Spittle AJ, Cheong JL, Doyle LW, Einspieler C. A systematic review of the predictive validity of observational early infant motor assessments for subsequent cerebral palsy. PROSPERO International prospective register of systematic reviews. 2016; Available from: http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42016042551.\u003c/li\u003e\n\u003cli\u003eThe Joanna Briggs Institute. The System for the Unified Management, Assessment and Review of Information (SUMARI) [Internet]. 2017 [cited 2019]. Available from: \u003ca href=\"https://www.jbisumari.org/\"\u003ehttps://www.jbisumari.org/\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. ICD-10 International Statistical Classification of Diseases and Related Health Problems: 10th Revision, Volume 2 Instruction Manual. www.who.int/classifications/icd/ICD-10_2nd_ed_volume2.pdf (Accessed on September 7, 2010)\u003c/li\u003e\n\u003cli\u003eRosenbaum P, Paneth N, Leviton A, et al. A report: the definition and classification of cerebral palsy April 2006. Dev Med Child Neurol Suppl. 2007;109:8-14.\u003c/li\u003e\n\u003cli\u003eEinspieler C, Prechtl HFR. Prechtlʼs assessment of general movements: A diagnostic tool for the functional assessment of the young nervous system. Ment.Retard.Dev.Disabil.Res.Rev. 2005;11(1):61-6\u003c/li\u003e\n\u003cli\u003eAltman, D. G., \u0026amp; Bland, J. M. (1994). Diagnostic tests. 1: Sensitivity and specificity. BMJ (Clinical research ed.), 308(6943), 1552. doi:10.1136/bmj.308.6943.1552\u003c/li\u003e\n\u003cli\u003eAltman, D. G., \u0026amp; Bland, J. M. (1994). Diagnostic tests 2: Predictive values. BMJ (Clinical research ed.), 309(6947), 102. doi:10.1136/bmj.309.6947.102\u003c/li\u003e\n\u003cli\u003eMoher D, Liberati A, Tetzlaff J, Altman DG. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. [Internet]. 2009 [cited 2019]. Available from: \u003ca href=\"http://prisma-statement.org/PRISMAStatement/FlowDiagram.aspx\"\u003ehttp://prisma-statement.org/PRISMAStatement/FlowDiagram.aspx\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMoola S, Munn Z, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Currie M, Qureshi R, Mattis P, Lisy K, Mu P-F. Chapter 7: Systematic reviews of etiology and risk. In: Aromataris E, Munn Z (Editors). Joanna Briggs Institute Reviewer's Manual. The Joanna Briggs Institute, 2017. Available from \u003ca href=\"https://reviewersmanual.joannabriggs.org/\"\u003ehttps://reviewersmanual.joannabriggs.org/\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Neonatal encephalopathy; general movement assessment; Prechtl; hypoxia-ischemia encephalopathy; cerebral palsy, infants/neonates, term babies, preterm babies, motor development.","lastPublishedDoi":"10.21203/rs.2.20965/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.20965/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground Prediction of long-term neurodevelopmental outcomes remains an elusive goal for neonatology. Clinical and socioeconomic markers have not proven to be adequately reliable. The limitation in prognostication includes those term and late-preterm infants born with neonatal encephalopathy. The General Movements Assessment tool by Prechtl has demonstrated reliability for identifying infants at risk for neuromotor impairment. This tool is non-invasive and cost-effective. The purpose of this study is to identify the published literature on how this tool applies to the prediction of cerebral palsy in term and late-preterm infants diagnosed with neonatal encephalopathy and so detect the research gaps. Methods We will conduct a systematic scoping review for data on sensitivity, specificity, positive and negative predictive value and describe the strengths and limitations of the results.\u0026nbsp;This review will consider studies that included infants more than or equal to 34+0 weeks gestational age, diagnosed with neonatal encephalopathy, with a General Movements Assessment done between birth to six months of life and an assessment for cerebral palsy by at least two years of age. Experimental and quasi-experimental study designs including randomized controlled trials, non-randomized controlled trials, before and after studies, interrupted time-series studies and systematic reviews will be considered. Case reports, case series, case control and cross-sectional studies will be included. Text, opinion papers and animal studies will not be considered for inclusion in this scoping review as this is a highly specific and medical topic. Studies in the English language only will be considered. Studies published from at least 1970 will be included as this is around the time when the General Movements Assessment was first introduced in neonatology as a potential predictor of neuromotor outcomes. We will search five databases (MEDLINE, Embase, PsychINFO, Scopus and CINAHL). Two reviewers will conduct all screening and data extraction independently. The articles will be categorized according key findings and a critical appraisal performed. Discussion The results of this review will guide future research to improve early identification and timely intervention in infants with neonatal encephalopathy at risk of neuromotor impairment.\u003c/p\u003e","manuscriptTitle":"The general movements assessment in term and late-preterm infants diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol.","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-03-30 15:46:03","doi":"10.21203/rs.2.20965/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accept","date":"2020-04-10T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-04-07T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept\nForm responses:\n---\n\nComments to Author:\n---\nNone* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"reviewerAgreed","content":"","date":"2020-03-31T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-03-27T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept\nForm responses:\n---\n\nComments to Author:\n---\nMinor changes request has been settled.* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"editorAssigned","content":"","date":"2020-03-26T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-03-26T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-03-26T12:00:00+00:00","index":1,"fulltext":""},{"type":"checksComplete","content":"","date":"2020-03-25T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-03-25T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-03-19 19:10:11","doi":"10.21203/rs.2.20965/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-03-24T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-03-23T12:00:00+00:00","index":2,"fulltext":"Recommendation: Minor Revision\nForm responses:\n---\n\nComments to Author:\n---\nI would suggest to replace preterm and term neonates/newborns with preterm and term infants in the manuscript. It was changed in title but not in the manuscript. Please replace where ever applicable.\n\nAuthors have included this statement in the method section of the manuscript \"Case reports, case series, case control and cross-sectional studies will be included.Animal studies - these were excluded\" but did not modified in the abstract\n\nReference 3 is not complete, please look into it.* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"reviewerAgreed","content":"","date":"2020-03-19T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorAssigned","content":"","date":"2020-03-17T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-03-17T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-03-17T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-03-17T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept\nForm responses:\n---\n\nComments to Author:\n---\nThe authors provided a detailed response and helpful track changes. The authors responded sufficiently to my comments. I recommend publication of the revised version.* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"checksComplete","content":"","date":"2020-03-16T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-03-16T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-01-15 22:37:04","doi":"10.21203/rs.2.20965/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-02-27T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-02-25T12:00:00+00:00","index":2,"fulltext":"Recommendation: Minor Revision\nForm responses:\n---\n\nComments to Author:\n---\nTitle: Well defined and easy to read; however, suggest replacing newborns with infants which is more suitable terminology.\n\nAbstract: Clear background with the objective of the scoping review, method section is well described\n\nBackground: It is well written; however, the references which are provided for the statement \"clinical and socioeconomic outcome markers have not proven to be adequately reliable\" do not appear relevant to me, I would consider using other references to support this statement.\n\nSuggest to elaborate more on neonatal encephalopathy regarding etiology and treatment, only HIE is well described and treatment is well explained but neonatal encephalopathy has wide variety and has been used throughout the manuscript as such suggest to elaborate more. If the goal is to only focus on hypoxic ischemic encephalopathy then use HIE instead of NE throughout the manuscript.\n\nSuggest using another reference to support the statement on Page 5, line 19 to 26. The reference number 12 is from book; consider adding another reference to support this statement as mentioned about the evidence from literature.\n\nVery nice summary was provided on all the systematic review on GMA. Suggest breaking down objective as primary and secondary objectives. Primary objective is the scope of the research with regards to the GMA and its ability to predict CP, in term and late-preterm newborns with a diagnosis of NE, and secondary objective is to identify the gaps in the literature.\n\nGood rational was provided regarding the choice of scoping review.\n\nEligibility criteria: Eligibility criteria is described in Table 2 and in text on page 10 from line 10 to 26, inclusion criteria was well described; however, I recommend to delete the statement about participants who do not meet the inclusion criteria such as \"Neonates \u003c 34+0 weeks GA, No diagnosis of NE, No GMA done between birth up to six months of age or with only automated application of GM. No assessment of CP by two years of age\". It is suggested to put only this statement as exclusion criteria\" Neonates born with: life threatening congenital abnormalities, congenital viral infections, an abnormal karyotype and metabolic disorders\".\n\nDefinitions of the concepts were explicitly defined.\n\nSearch Strategy: It is well described; however suggest to provide more information about what other type of studies will not be included such as case report, case series, case control and cross sectional studies. Also consider adding the statement that animal studies will be excluded.\n\nOn page 13, line 15, other key terms need to be considered are infants/neonates, term babies, preterm babies, Encephalopathy, cerebral palsy and motor development.\n\nAdequate information was provided on study selection.\n\nData extraction, analysis and synthesis: Good description was provided about how the screening of the citations will be done. I suggest adding the statement about full text screening and keeping track of excluded studies with reason of exclusion. Data extraction will be likely started after full text screening is done unless initial data extraction is done during full text screening and final data extraction is done for only included studies.\n\nDiscussion: This section is well described; however, I would add more information about how this review will help us to identify the gaps in research.\n\nIn section - what this study will add, another objective is presented which is \"identify gaps in the setting of NE management with therapeutic hypothermia\" which is different then what was mentioned in objective section. I suggest to look in to the terminology used such NE and HIE and use the consistent terminology as there are many causes of NE and hypoxic ischemic event is one of the causes of NE.\n\n\n* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests'**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"reviewerAgreed","content":"","date":"2020-02-12T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-02-12T12:00:00+00:00","index":3,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-02-03T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-02-03T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-02-03T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept\nForm responses:\n---\n\nComments to Author:\n---\nSystematic Reviews: The general movements assessment in term and late-preterm newborns diagnosed with neonatal encephalopathy, as a predictive tool of cerebral palsy by two years of age: a scoping review protocol.\n\nGENERAL COMMENT\nThe authors should be congratulated for their protocol which is very well prepared. Thus, I strongly suggest to publish the protocol. The authors may decide whether they want to adopt my suggestions. I am not completely sure if it is acceptable about announcing instead of reporting the individual search strategies.\n\nABSTRACT: \"describe the strengths and limitations of the results\"\nCOMMENT: Possible language issue: I usually try to describe the strengths and limitations of the methods or procedures to arrive at conclusions on the credibility of the results.\n\nABSTRACT: \"This review will consider studies that included neonates more than or equal to 34+0 weeks gestational age, diagnosed with neonatal encephalopathy, with a General Movements Assessment done between birth to six months of life and an assessment for cerebral palsy by at least two years of age.\"\nCOMMENT: I think, this is a concise and clearly written statement.\n\nMETHODS: \"Table 3. Definitions of concepts\"\nCOMMENT: The concepts are sufficiently well described and provided with appropriate references.\n\nMETHODS: \"A search strategy will be developed\"\nCOMMENT: The individual search strategies concerning all databases might be reported in the protocol. Thus, I am not sure whether this announcement is acceptable.\n\nAPPENDIX I: \"Table 1. Summary of reviews on the general movement assessment and its predictive value for neuromotor outcomes\"\nCOMMENT: The table is well prepared and contains seminal and recent reviews.\n\nREFERENCES\nCOMMENT: You may want to check another review: Heineman 2008 (PMID: 18698195). You may want to check these articles which express some skeptical issues: Maitre 2018 (PMID: 29517109), Rosenbloom 2018 (PMID 29105750), and Shepherd 2018 (PMID: 29926474). You may want to check some other references during the preparation of the review: Adde 2007 (PMID: 16650949), Einspieler 2016 (PMID 26365130), Ferrari 2002 (PMID: 11980551), George 2015 (PMID: 26377791), Haataja 2016 (PMID: 26347464), Hadders-Algra 2014 (PMID: 25309506), Morgan 2019 (PMID: 31694305), Olsen 2018 (PMID: 28940492), Philippi 2014 (PMID: 24844774), Ricci 2018 (PMID 29144840).\n* Level of interest: **An article of importance in its field that should be highlighted to relevant networks**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests.**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n* Were you mentored through this peer review?: **No**\n"},{"type":"editorAssigned","content":"","date":"2020-01-29T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-01-28T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-01-14T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-01-13T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"systematic-reviews","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sysr","sideBox":"Learn more about [Systematic Reviews](http://systematicreviewsjournal.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/sysr/default.aspx","title":"Systematic Reviews","twitterHandle":"@MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f95e90e-7bec-4e3f-a8d0-8125e4d2ff67","owner":[],"postedDate":"March 30th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":72506,"name":"Obstetrics \u0026 Gynecology"}],"tags":[],"updatedAt":"2020-07-05T15:01:29+00:00","versionOfRecord":{"articleIdentity":"rs-11563","link":"https://doi.org/10.1186/s13643-020-01358-x","journal":{"identity":"systematic-reviews","isVorOnly":false,"title":"Systematic Reviews"},"publishedOn":"2020-07-04 12:00:00","publishedOnDateReadable":"July 4th, 2020"},"versionCreatedAt":"2020-03-30 15:46:03","video":"","vorDoi":"10.1186/s13643-020-01358-x","vorDoiUrl":"https://doi.org/10.1186/s13643-020-01358-x","workflowStages":[]},"version":"v3","identity":"rs-11563","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-11563","version":["v3"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}