Topographic screening of donor eyes for previous refractive surgery
Bibliographic record
Abstract
PURPOSE: To determine whether donor eyes had previous refractive surgery using Orbscan (Bausch & Lomb Surgical) corneal topography. SETTING: Lions Eye Bank of Oregon, Portland, Oregon, USA, and Maisonneuve-Rosemont, Hospital, Montreal, Quebec, Canada. METHODS: Orbscan corneal topographies of 50 donor eyes from the Lions Eye Bank of Oregon were obtained; 10 eyes had previous refractive surgery (6 laser in situ keratomileusis, 2 photorefractive keratectomy, 2 radial keratotomy) to correct myopia, and 40 had not had surgery. Algorithms based on corneal anterior and posterior elevations and anterior tangential curvature were developed: The difference in curvature (DC) was based on the difference in the mean anterior tangential curvature between central and midperipheral areas; difference in elevation (DE) represented the difference between the anterior and posterior central elevations. Receiver-operating characteristic (ROC) curves for each algorithm were obtained, and sensitivity values at fixed specificities were calculated. RESULTS: The mean area under the ROC curve, which corresponds to the probability of correctly identifying the presence of a previous refractive surgery, was 0.853 +/- 0.079 (SE) for DC and 0.933 +/- 0.057 for DE. The DC algorithm resulted in a sensitivity of 80% for a specificity of 87.5%, and DE yielded a sensitivity of 90% for a specificity of 92.5%. There was a strong correlation between the value of the DE and DC algorithms and the amount of previous refractive surgery (DC: r = 0.84, P = .008; DE: r = 0.76, P = .028). CONCLUSION: The results led to a proposed criteria-based system using Orbscan corneal topography to screen eye-bank eyes for previous refractive surgery.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".