The cost‐effectiveness of alternative vision screening models among preschool children in rural China
Bibliographic record
Abstract
PURPOSE: To explore the accuracy and cost-effectiveness of three vision screening models among preschool children in rural China. METHODS: Vision screening was carried out among children aged 4-5 years in 65 preschools in two counties in Northwest China, using Crowded Single Lea Symbols to test visual acuity. Children were assigned randomly by school to one of three screening models: screening by teachers (15 schools, 1835 children), local optometrists (30 schools, 1718 children) or volunteers (20 schools, 2183 children). Children identifying ≥2 symbols incorrectly in either eye failed screening. Accuracy of screening was compared with screenings executed by experienced optometrists among 141 children selected randomly from the three screening models. Direct and indirect costs for each model were assessed. Costs to detect a true case failed screening were estimated. RESULTS: The sensitivity for three models ranged from 76.9% to 87.5%, specificity from 84.9% to 86.7% and standardized positive predictive value from 83.7% to 85.7%. None differed significantly between models. The costs per case detected were $37.53, $59.14 and $52.19 for the teachers, local optometrists and volunteers. In producing the cost estimates for teacher screening and local optometrist screening models, we used a salary payment that was identical for both models (with the salary being equal to that of the optometrist). The teacher screening model was the most cost-effective. CONCLUSION: Accuracy of screening by teachers, local optometrists and volunteers was the same in this setting, but the use of teachers was most cost-effective, reducing the cost per case detected by almost 40%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".