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Record W2976724290 · doi:10.1136/bmjopen-2019-032138

Choosing appropriate tools and referral criteria for vision screening of children aged 4–5 years in Canada: a quantitative analysis

2019· article· en· W2976724290 on OpenAlexafffundabout
Mayu Nishimura, Agnes Wong, Ashley Cohen, Kevin E. Thorpe, Daphne Maurer

Bibliographic record

VenueBMJ Open · 2019
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsPublic Health OntarioUniversity of TorontoMcMaster UniversityHospital for Sick Children
FundersCanadian Institutes of Health ResearchUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaPrevent Blindness America
KeywordsMedicineReferralFamily medicineEpidemiologyPublic healthBiostatisticsOptometryPediatricsGerontologyNursingPathology

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess the diagnostic accuracy of five vision screening tools used in a school setting using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). DESIGN: We compared the results of the five best evidence-based screening tools available in 2014 to the results of a comprehensive eye exam with cycloplegic refraction by a licenced optometrist. Screening included Cambridge Crowded Acuity Cards, Plusoptix S12 and Spot photoscreeners, Preschool Randot Stereoacuity Test and the Pediatric Vision Scanner (PVS). Referral criteria followed AAPOS (2013) guidelines and published norms. SETTING: A large school in Toronto, Canada, with 25 split classrooms of junior kindergarten (JK: 4 year olds) and senior kindergarten (SK: 5 year olds) children. PARTICIPANTS: Over 2 years, 1132 eligible children were enrolled at the school. After obtaining parental consent, 832 children were screened. Subsequently, 709 children had complete screening and optometry exam data. MAIN OUTCOME MEASURES: The presence/absence of a visual problem based on optometrist's assessment: amblyopia, amblyopia risk factors (reduced stereoacuity, strabismus and clinically significant refractive errors) and any other ocular problem (eg, nystagmus). RESULTS: Overall, 26.5% of the screened children had a visual problem, including 5.9% with amblyopia. Using all five tools, screening sensitivity=84% (95% CI 78 to 89), specificity=49% (95% CI 44 to 53), PPV=37% (95% CI 33 to 42), and NPV=90% (95% CI 86 to 93). The odds of having a correct screening result in SK (mean age=68.2 months) was 1.5 times those in JK (mean age=55.6 months; 95% CI 1.1 to 2.1), with sensitivity improved to 89% (95% CI 80 to 96) and specificity improved to 57% (95% CI 50 to 64) among SK children. CONCLUSIONS: A school-based screening programme correctly identified 84% of those kindergarten children who were found to have a visual problem by a cyclopleged optometry exam. Additional analyses revealed how accuracy varies with different combinations of screening tools and referral criteria.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.155
GPT teacher head0.479
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations20
Published2019
Admission routes3
Has abstractyes

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