Bibliographic record
Abstract
PURPOSE: To determine the risk factors for an epithelial defect during laser in situ keratomileusis (LASIK). SETTING: LASIK Vision, Toronto, Ontario, Canada. METHODS: In this prospective cohort study, 926 patients (1852 eyes) presenting for LASIK over a 6-month period were evaluated for age, sex, Fitzpatrick Skin Type (FST), eye color, hair color at 3 years of age, facial skin wrinkling, ethnicity (Lancer Ethnicity Scale [LES]), keratometry, Schirmer I reading, corneal thickness, and preexisting signs of corneal epithelial dystrophy. In all patients, LASIK was performed using the Technolas 217 laser (Bausch & Lomb), the Hansatome(R) microkeratome (Bausch & Lomb), and the same nomogram settings. RESULTS: Fourteen percent of patients had significant epithelial fragility. In patients with FST I or II or LES 1 or 2, the relative risk of an epithelial defect was 10 times greater than in other patients; in those older than 40 years, it was 6 times greater than in other patients; in those with lighter hair or eye color, it was 2 to 3 times greater than in patients with darker hair or eyes. There was no significant difference in pachymetry, vertical or horizontal keratometry, or Schirmer readings between eyes with epithelial defects and eyes without. CONCLUSIONS: The risk of epithelial erosions during LASIK strongly correlated with patients' skin type and age.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".