Visual Acuity Screening in North Indian Schools: Testing Accuracy and Cost of Alternate Screening Models
Bibliographic record
Abstract
Purpose Our study compares the sensitivity, specificity and cost of visual acuity screening as performed by all class teachers (ACTs), selected teachers (STs) and vision technicians (VTs) in north Indian schools.Methods Prospective cluster randomized control studies are conducted in schools in a rural block and an urban-slum of north India. Consenting schools, with a minimum of 800 students aged 6 to 17 years, within a defined study region in both locations, were randomised into three arms: ACTs, STs or VTs. Teachers were trained to test visual acuity. Reduced vision was defined as unable to read equivalent of 20/30. Optometrists, who were masked to results of initial screening, examined all children. Costs were measured for all three arms.Results The number of students screened were 3410 in 9 ACT schools, 2999 in 9 ST schools and 3071 in 11 VT schools. Vision deficit was found in 214 (6.3%), 349 (11.6%) and 207 (6.7%), (p < .001) children in the ACT, ST and VT arms, respectively. The positive predictive value of VT screening for vision deficit (81.2%) was significantly higher than that of ACTs (42.5%) and STs (30.1%), (p < .001). VTs had significantly higher sensitivity of 93.3% and specificity of 98.7%, compared to ACTs (36.0% and 96.1%) and STs (44.3% and 91.2%). The cost of screening children with actual visual deficit by ACTs, STs and VTs, was found to be $9.35, $5.79 and $2.82 per child, respectively.Conclusion Greater accuracy and lower cost favours school visual acuity screening by visual technicians in this setting, when they are available.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".