Effect of Laser in situ Keratomileusis on Tear Secretion and Corneal Sensitivity
Bibliographic record
Abstract
PURPOSE: To study changes in corneal sensitivity and Schirmer I scores following laser in situ keratomileusis (LASIK) and the correlation between the two. METHODS: Twenty-three patients who had LASIK at The Gimbel Eye Center, Toronto, Ontario, Canada, participated in the study. All were asymptomatic for severe dry eyes before surgery. All patients underwent a Schirmer test (without anesthetic), a filament corneal sensitivity test, and slit-lamp microscopy including staining with lissamine green preoperatively and at postoperative time intervals of 3 to 5 days, and 1 and 3 months. RESULTS: No correlation was found between the difference in Schirmer test scores and the difference in corneal sensitivity, at any timepoint. A non-statistically significant trend toward a reduction in Schirmer values immediately after surgery was noted, with a return to slightly lower than baseline levels by 3 months. Corneal sensitivity was significantly decreased immediately after surgery and returned to preoperative levels by 3 months (P<.0001). There was a statistically significant effect of age, gender, and mean spherical equivalent refraction on corneal sensitivity (P<.0001) and a significant effect of age on the time trend (P=.02), but not for Schirmer levels or staining. CONCLUSIONS: A significant reduction in corneal sensitivity immediately following surgery occurred, with a return to preoperative levels by 3 months. Schirmer test scores similarly decreased, although without statistical significance, and returned to near preoperative levels after 3 months. A statistically significant correlation between the reduction in tearing and reduction in corneal sensitivity after LASIK was not demonstrated.
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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 | 0.000 |
| 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) | 0.001 | 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".