The Impact of Hyperopia on Academic Performance Among Children: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To assess the impact of uncorrected hyperopia and hyperopic spectacle correction on children's academic performance. DESIGN: Systematic review and meta-analysis. METHODS: We searched 9 electronic databases from inception to July 26, 2021, for studies assessing associations between hyperopia and academic performance. There were no restrictions on language, publication date, or geographic location. A quality checklist was applied. Random-effects models estimated pooled effect size as a standardized mean difference (SMD) in 4 outcome domains: cognitive skills, educational performance, reading skills, and reading speed. (PROSPERO registration: CRD-42021268972). RESULTS: Twenty-five studies (21 observational and 4 interventional) out of 3415 met the inclusion criteria. No full-scale randomized trials were identified. Meta-analyses of the 5 studies revealed a small but significant adverse effect on educational performance in uncorrected hyperopic compared to emmetropic children {SMD -0.18 [95% confidence interval (CI), -0.27 to -0.09]; P < 0.001, 4 studies} and a moderate negative effect on reading skills in uncorrected hyperopic compared to emmetropic children [SMD -0.46 (95% CI, -0.90 to -0.03); P = 0.036, 3 studies]. Reading skills were significantly worse in hyperopic than myopic children [SMD -0.29 (95% CI, -0.43 to -0.15); P < 0.001, 1 study]. Qualitative analysis on 10 (52.6%) of 19 studies excluded from meta-analysis found a significant (P < 0.05) association between uncorrected hyperopia and impaired academic performance. Two interventional studies found hyperopic spectacle correction significantly improved reading speed (P < 0.05). CONCLUSIONS: Evidence indicates that uncorrected hyperopia is associated with poor academic performance. Given the limitations of current methodologies, further research is needed to evaluate the impact on academic performance of providing hyperopic correction.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".