Subjective and objective evaluation of corneal haze after accelerated corneal crosslinking for corneal ectasias
Bibliographic record
Abstract
Abstract Purpose To evaluate the relationship between subjective (slit lamp examination [SLE]) and objective (densitometry) measurements of corneal haze after accelerated corneal crosslinking (aCXL), assess the relationship between densitometry and corrected distance visual acuity (CDVA), and determine the effect of baseline characteristics on densitometry after aCXL in eyes with progressive keratoconus and other ectasias. Setting Kensington Eye Institute and Bochner Eye Institute, Toronto, Canada. Design Retrospective analysis of a prospective interventional cohort study. Methods Scheimpflug‐derived corneal densitometry, CDVA, maximum keratometry ( K max ), and central corneal thickness were measured preoperatively and up to 1 year after aCXL, and post‐operative haze was estimated with SLE ( n = 483 eyes). A random effect model was used to examine the relationship between post‐operative subjective haze with SLE and densitometry. Linear mixed models were used to examine the relationship between densitometry, pre‐operative baseline characteristics, and CDVA. Results There was a significant association between subjective haze with SLE and densitometry ( p < 0.001). There was a significant relationship between CDVA and densitometry: for every 10 GSUs of increased densitometry in the 0–2 mm zone, CDVA worsened by approximately half a Snellen line ( p < 0.001). Age and pre‐operative K max were significant predictors of densitometry. For every 10 years of age, densitometry increased by 0.68 GSUs (95% CI [0.27 to 1.07], p < 0.001). For every 10 D of increased preoperative K max , densitometry increased by 0.69 GSUs (95% CI [0.41 to 0.98], p < 0.001). Conclusions Subjective haze after aCXL estimated with SLE, is significantly associated with densitometry. Increased densitometry after aCXL is associated with a reduction in CDVA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".