Color vision screening of school children in India using the CVTMET
Bibliographic record
Abstract
Purpose: The prevalence of red-green color vision defects in India has been reported to range from 2.5% to 7.5% in men and 0.13% to 1.04% in women. The lowest prevalence was found in certain tribal groups. Although one would expect the prevalence to be similar in children, little modern data is available. This study was carried out to determine the prevalence of red-green color deficiencies in Indian school children using the Color Vision Test Made Easy (CVTMET). Methods: Children between the ages of 4 and 9 years were screened at different schools in Mumbai using the CVTMET. Time taken to complete the test was recorded. Of the 1711 children, 33 (ages of 4–5 years) were excluded due to difficulty in interpreting their responses. In the remaining 1678, 1002 were males and 676 were females. Results: Eighteen males and six females failed the CVTMET. This results in a prevalence in males of 1.8% (95% CI 1.1% to 2.8%) and 0.89% (95% CI 0.4% to 1.9%) in females. Those who failed the CVTMET took significantly more time 215.20 (+102.92) seconds compared to color-normals 120.22 (+66.71) seconds (p<0.001). Conclusions: The rate in male children was lower than the 3.7% to 7.5% values reported for nontribal urban groups, whereas the female rate fell within the range of previous reports. The low prevalence in males suggests that additional work is required to determine the validity of the CVTMET test and/or to determine the prevalence of red-green defects in the modern Indian society.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".