Visual acuity screening in schools: A systematic review of alternate screening methods
Bibliographic record
Abstract
Purpose: Visual acuity (VA) screening in schools has been widely adopted by eye programs around the world. This review evaluates the efficacy and cost of alternate VA screening methods to identify school-age children with undetected visual deficits due to refractive error and other visual disorders. Methods: Published studies were identified from Ovid MEDLINE, MEDLINE In-Process, and EMBASE for trials from 1974 to March 2015 as well as from reference and author searches. All controlled studies were included. Data extraction tables were developed a priori for key screening test performance indicators, including compliance. Results: Three trials met the inclusion criteria, two comparing alternate teacher models and one compared teachers to primary eye care workers using three different VA thresholds. School vision screening using “all class teachers” (ACTs) found significantly fewer screen-positive children than select teachers (STs) (9.9 vs. 16.6% [p < 0.001] respectively) and significantly more children with visual disorders (5.7 vs. 4.0% [p < 0.001] respectively) at 30% of the cost and improved compliance. Teachers performed similarly to primary eye care workers in detecting children with visual disorders with 6/12 the optimal cut-off level. Conclusions: Using detection of children with visual disorders as outcome, evidence supports school screening using “ACTs” and a 6/12 VA threshold. Using the proportion of students with visual disorders who attend follow up at the referral hospital within three months as outcome, one study supports use of ACTs. Using cost per child detected with visual disorders as outcome, one study supports use of ACTs.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".