Frequency and Distribution of Corneal Astigmatism and Keratometry Features: Methodology and Findings of the UK Biobank Study
Bibliographic record
Abstract
Abstract Purpose To describe corneal astigmatism in the UK Biobank population, to look for associations with other biometric variables and socio-demographic factors, and to report the proportion with abnormal keratometry and irregular astigmatism suggestive of pathological corneal ectasias such as keratoconus. Methods Cross-sectional data were obtained from UK Biobank ( www.ukbiobank.ac.uk/ ). A subsample of 107,452 participants from UK communities had undergone an enhanced ophthalmic examination including autorefractor keratometry (Tomey RC 5000, Tomey Corp., Nagoya, Japan). After quality control and applying relevant exclusions, data on corneal astigmatism on 83,751 participants was available for analysis. Potential associations were tested through univariable regression and significant parameters carried forward for multivariable analysis. Results In a univariable analysis, the characteristics significantly protective against corneal astigmatism were gender (male), older age, darker skin colour and increased alcohol intake (all p<0.001). The parameters significantly associated with increased corneal astigmatism were older age at completion of full time education, use of UV protection and lower corrected visual acuity. After inclusion in the multivariable analysis, age, gender, age at completion of full time education, corrected visual acuity and skin colour remained significant (all p<0.001). Increased corneal astigmatism was also found to be significantly associated with amblyopia or strabismus. No individuals with abnormal keratometry or irregular astigmatism were reported. Conclusions This analysis of associations with astigmatism in a large cohort of volunteers confirms previous associations including adverse associations with younger age and female gender, and identified novel associations including darker skin colour and frequency of alcohol intake. The highest risk group for corneal astigmatism were younger females of lighter skin colour, having completed full time education later, with higher logMAR corrected visual acuity. We also confirmed that corneal astigmatism is a high risk factor for amblyopia and strabismus. Finally since no cases of keratoconus were identified, this would suggest that simple keratometry indices may not be sufficient for population screening of keratoconus.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".