MétaCan
Menu
Back to cohort

Accuracy and Utility of Self-report of Refractive Error

2016· article· en· W2407124630 on OpenAlexfundno aff
Phillippa Cumberland, Antonietta Chianca, Jugnoo S. Rahi

Bibliographic record

VenueJAMA Ophthalmology · 2016
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersMoorfields Eye Hospital NHS Foundation TrustNewcastle UniversityQueen's University BelfastUniversity College LondonUniversity of SouthamptonUniversity of BristolKingston UniversityUniversity of East AngliaNational Institute for Health and Care ResearchQueen's UniversityNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchGreat Ormond Street Hospital for ChildrenKing's College LondonUniversity of Dundee
KeywordsMedicineOptometryRefractive errorOphthalmologyVisual acuity

Abstract

fetched live from OpenAlex

IMPORTANCE: Large-scale generic studies offer detailed information on potential risk factors for refractive error across the life course, but ophthalmic examination in such cases to determine the refractive error phenotype is challenging and costly. Thus, refractive status is commonly assigned using questionnaires. In a population survey, often only a few condition-specific self-reported questions can be included, so the questions used must be effective in ruling in those who have the trait of interest and ruling out those who do not. OBJECTIVE: To determine the accuracy of identification of refractive status using self-reported age at and/or reason for first use of glasses or contact lenses (optical correction). DESIGN, SETTING, AND PARTICIPANTS: The UK Biobank study, a cross-sectional epidemiologic study, included 117 278 participants aged 40 to 69 years in 6 regional centers in England and Wales. Data for the present study were assessed from June 2009 to July 2010. Patients underwent autorefraction measurement. Spherical equivalent in the more extreme eye was used to categorize myopia (-1.00 diopter [D] or more extreme) and hypermetropia (+1.00 D or more extreme). MAIN OUTCOMES AND MEASURES: Sensitivity and specificity of the reason for optical correction were assessed using autorefraction as the gold standard. Receiver operating characteristic curves assessed the accuracy of self-reported age at first use of optical correction and incremental improvement with addition of the reason. RESULTS: Of the 95 240 participants who reported using optical correction (55.6% female; mean [SD] age, 57.7 [7.5] years), 92 121 (96.7%) provided their age at first use and 93 156 (97.8%) provided the reason. For myopia, sensitivity of the reason for optical correction was 89.1% (95% CI, 88.7%-89.4%), specificity was 83.7% (95% CI, 83.4%-84.0%), and positive and negative predictive values were 72.7% (95% CI, 72.2%-73.1%) and 94.0% (95% CI, 93.8%-94.2%), respectively. The area under the curve was 0.829 (95% CI, 0.826-0.831) and improved to 0.928 (95% CI, 0.926-0.930) with combined information. By contrast, self-report of the reason for optical correction of hypermetropia had low sensitivity (38.1%; 95% CI, 37.6%-38.6%), and the area under the curve with combined information was 0.713 (95% CI, 0.709-0.716). CONCLUSIONS AND RELEVANCE: In combination, self-report of the reason for and age at first use of optical correction are accurate in identifying myopia. These findings indicate an agreed set of questions could be implemented effectively in large-scale generic population-based studies to increase opportunities for integrated research on refractive error leading to development of novel prevention or treatment strategies.

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)0.026
metaresearch head score (Gemma)0.114
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 score0.026
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.114
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.049
GPT teacher head0.389
Teacher spread0.340 · 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

Citations35
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJAMA OphthalmologySame topicOphthalmology and Visual Impairment StudiesFrench-language works237,207