Optical correction of hyperopia in school-aged children: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Prescribing patterns for hyperopia in children vary widely among eye care providers worldwide. This scoping review aims to identify and map the current literature on optical correction and catalogue outcomes reported, particularly in the domains of vision, vision-related functional outcomes and quality of life (QoL) in school-aged children with hyperopia. METHODS AND ANALYSIS: This protocol was developed in accordance with the Joanna Briggs Institute's Manual for Evidence Synthesis. We will include studies involving school-aged children with hyperopia without restrictions on sex, gender, race, ethnicity, type of optical correction, length of intervention, publication date or country of origin. We will include studies with internal or external comparison groups. We will exclude studies associated with myopia control treatments, ocular and visual pathway pathologies affecting vision or visual function. We will search Cochrane CENTRAL, Embase.com and PubMed. Examples of data to be extracted include population demographics, visual acuity, study-specific definitions for refractive error, treatment regimens for optical correction, vision and vision-related functional outcomes and QoL (general or vision-related) as quantified by validated instruments. ETHICS AND DISSEMINATION: Informed consent and Institutional Review Board approval will not be required, as this scoping review will only use published data. The results from the scoping review will be disseminated by publication in a peer-reviewed scientific journal and at professional conferences.
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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 | 0.055 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.009 |
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".