Screening of visual disorders among high school students without expressed complaint
Bibliographic record
Abstract
Abstract Purpose Many students understate their visual discomfort, although it may have an educational impact. We studied the prevalence of visual disorders among students without self‐reported complaints. Methods Four hundred students between 15 and 22 years of age responded to a questionnaire followed by a visual screening (refraction and binocular vision) in order to detect any visual discomfort that they might be unaware of. When visual problems were detected, the participants were suggested to have an ophthalmology and orthoptic assessment. Results Visual disorders were found in 346 students. Thirty‐two percent of them agreed to have an ophthalmology and orthoptic assessment. Best‐corrected visual acuity was 20/20 in more than 95% of them. Nearly 60% were hyperopes, from which more than two thirds required an optical correction. Over or under‐corrected myopia and uncorrected astigmatism were uncommon. Convergence insufficiency was found in 80%, and only a few ophthalmologic pathologies were diagnosed. Conclusion Many students have vision problems to which they are accustomed. It was rare to find underlying ophthalmologic pathologies (strabismus, nystagmus, ...). In most cases, these functional symptoms are due to under‐corrected hyperopia, possibly associated with convergence insufficiency. As visual demand is lower during primary education, some visual discomfort may only become symptomatic during the following years. However, the nature of these disorders suggests that they may interfere with education. We are now investigating the prevalence of learning difficulties among these participants, and whether there is an improvement after intervention.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".