Laser in situ keratomileusis in patients with diabetes
Bibliographic record
Abstract
PURPOSE: To study the outcomes of laser in situ keratomileusis (LASIK) in patients with well-controlled diabetes mellitus. SETTING: Gimbel Eye Centres, Calgary and Edmonton, Canada. METHODS: The charts of all patients with diabetes who had LASIK surgery at the Gimbel Eye Centres were reviewed retrospectively. Uncorrected visual acuity (UCVA), best spectacle-corrected visual acuity (BSCVA), final refraction, and complications were assessed. RESULTS: Twenty-four patients (16 men and 8 women) were identified. Mean patient age was 42 years (range 24 to 57 years). Seventeen patients had diabetes type II, and 7 had diabetes type I. Mean preoperative spherical equivalent (SE) was -4.88 +/- 2.13 diopters (D) (range +1.625 to -9.00 D). Median follow-up was 6 months (range 4 to 44 months). No eye lost BSCVA. Twenty-nine eyes (63%) achieved UCVA 20/25 or better, and 31 eyes (67%) were within +/-0.5 D of the intended refraction after the first LASIK surgery. Retreatment was required in 13 eyes (28.3%) because initial surgery was not adequate to correct the refractive error. At the last follow-up visit, 40 eyes (87%) achieved UCVA of 20/25 or better and 43 eyes (93.5%) were within +/-0.5 D of the intended refraction. Three eyes (6.5%) developed an epithelial defect after surgery, and secondary epithelial ingrowth developed in 2 of these eyes. No advancement of diabetic retinopathy was noticed in any eye at the end of the follow-up period. CONCLUSIONS: Laser in situ keratomileusis surgery was safely performed in patients with well-controlled diabetes. Enhancement may often be required for optimal correction.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".