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Screening of visual disorders among high school students without expressed complaint

2013· article· en· W2043730654 on OpenAlexaff
C KOVARSKI, C CARLU, Serge Portalier, Caroline Faucher, J L Dufier, O. Roche, Christophe Orssaud

Bibliographic record

VenueActa Ophthalmologica · 2013
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsOrthopticConvergence insufficiencyStrabismusMedicineVisual acuityNystagmusOptometrySubjective refractionVisual impairmentOphthalmologyAudiologyRefractive errorPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.080
GPT teacher head0.446
Teacher spread0.366 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2013
Admission routes1
Has abstractyes

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