Association between sociodemographic factors, visual acuity and corneal topographic outcomes after collagen cross-linking in patients with keratoconus
Bibliographic record
Abstract
ABSTRACT Purpose To determine the association between sociodemographic factors, visual acuity (VA) and corneal topography (Kmax and K2) following collagen cross-linking (CXL) in a cohort of ethnically-diverse keratoconus patients. Methods The records of 88 keratoconus patients who underwent CXL between January 2021 and December 2022 at King’s College Hospital were examined. Data on age, sex, ethnicity, deprivation deciles, significant postoperative complications, pre and postoperative best VA (BVA), Kmax and K2 were extracted. Univariate Kaplan-Meier and multivariate cox-regression survival analyses were used to determine outcomes of BVA stability, Kmax stability K2 stability and composite Kmax/K2 stability at 52 weeks after CXL. Results At 52 weeks, there was an 81.1% (95% confidence interval (CI): 71.2%–87.6%) probability of BVA stability and 80.7% (67.4%–88.5%) probability of Kmax/K2 stability. Multivariate analyses showed significant associations ( p <0.05) between Kmax/K2 stability and ‘other’ ethnicity but not other sociodemographic factors. Conclusion CXL is associated with the stabilisation of vision and corneal topography in the majority of eyes. One significant association was found between other ethnicity and corneal topographical outcomes after CXL, however, no other significant associations were found between the majority of sociodemographic or economic factors with VA or corneal topographical outcomes after CXL. This suggests CXL results in good functional and anatomical outcomes independent of sociodemographic status.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".