Prevalence of oculomotor, binocular vision anomalies and refractive error among children with cerebral palsy in WHO South-East Asia: A protocol of systematic review and meta-analysis
Bibliographic record
Abstract
Introduction: Children with cerebral palsy (CP) may experience a variety of visual abnormalities, which might hamper their daily activities. Most physical therapy for the CP population focuses on visual aspects, which postpone rehabilitation outcomes. Considering the significance of vision to the CP community, we aimed to conduct a systematic review of the prevalence of ocular abnormalities such as oculomotor abnormalities, refractive errors, and binocular vision anomalies in children with Cerebral palsy in the absence of eye injury in WHO South-East Asia region. Methods & analysis: This systematic review and meta-analysis protocol are reported as per the PRISMA- P and MOOSE guidelines. A complete search strategy will be framed using MeSH terms and the opinion of the subject expert. A detailed search on PubMed, the Cochrane Library, Scopus, Web of Science and CINHAL will be carried out to retract the data on the prevalence of visual problems in the CP population (age< 18 years), published in English between January 2010 and 2024. Covidence software will be used to manage data, screen records and extract the information. The Newcastle-Ottawa Scale will be used to evaluate the listed studies quality and risk of bias. RevMan V.5 will be used to analyse the data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.093 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.020 | 0.031 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".