Topography-based screening for previous laser in situ keratomileusis to correct myopia
Bibliographic record
Abstract
PURPOSE: To demonstrate the feasibility of developing a screening tool based on corneal topography to detect previous myopic laser in situ keratomileusis (LASIK). SETTING: Clinical data from a private clinic analyzed in a university setting. METHODS: Two hundred thirty-three topographies were randomly selected so 1 topography per patient was used: 150 from unoperated corneas and 83 from corneas that had LASIK to correct myopia. The mean surgical correction was -4.40 diopters (D) +/- 2.53 (SD) (range -11.00 to -0.38 D). All topographies were performed using an Orbscan II unit (Bausch & Lomb Surgical). The LASIK procedures were performed using a Technolas 217C excimer laser and a Hansatome microkeratome (Bausch & Lomb Surgical). The algorithms used the mean value of the directional derivative (DT) of the anterior tangential curvature of the cornea in the 2.2 mm radius central disk and the mean value of the anterior elevation (E) with respect to the best-fit sphere in the 0.5 mm radius central disk. Topographies in the testing set (n = 119) were classified as operated if E < 0 (E algorithm) or DT > 0 (DT algorithm) or as unoperated. RESULTS: The E algorithm yielded 0% false positives and 16.7% false negatives and the DT algorithm, 6.5% and 7.1%, respectively. For myopia greater than -1.12 D, the DT algorithm provided a 0% false negative rate. The performance of E and DT algorithms, used in combination, was superior to clinical assessment. CONCLUSION: Criteria based on Orbscan II corneal topography are proposed for the detection of previous myopic LASIK performed with a Technolas 217C excimer laser.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".