Clinically significant laser in situ keratomileusis flap striae
Bibliographic record
Abstract
PURPOSE: To describe the incidence, risk factors, and outcomes before and after irrigation of clinically significant laser in situ keratomileusis (LASIK) flap striae. SETTING: Multisurgeon multicenter standardized protocol practice. DESIGN: Retrospective case-control series. METHODS: Eyes with striae necessitating flap relift and irrigation were identified. Preoperative, intraoperative, and postoperative variables were collected. Incidence, risk factors, and outcomes were assessed. RESULTS: = 0.9674; P < .001). Striae induced a small hyperopic shift that reversed after the relift (mean 0.22 diopter [D] ± 0.52 [SD] versus -0.02 ± 0.45 D) (P < .001). After relifting, 68.0%, 87.0%, and 96.0% of eyes had an uncorrected distance visual acuity (UDVA) of 20/20, 20/25, 20/40 or better versus 25.0%, 55.0%, and 84.0%, respectively, before the relift (P < .001). Thirteen percent fewer striae-treated eyes achieved a UDVA of 20/20. Before relifting, 51.0% of striae eyes lost 1 or more lines of corrected distance visual acuity, with a safety index reverting to control values (0.99 versus 1.00) (P > .05) after the relift. A laser refractive enhancement was performed in 6.28% of relifted striae eyes versus 3.04% in nonstriae control eyes. CONCLUSIONS: Flap striae requiring surgeon intervention occurred in 0.79% of eyes. Higher preoperative SE values were associated with an exponential increase risk for striae. Treatment by lifting and irrigation significantly improved the accuracy, efficacy, and safety to a level close to that of contralateral control eyes, although striae-treated eyes were more likely to need excimer laser retreatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".