Late Traumatic Displacement of Laser In Situ Keratomileusis Flaps
Bibliographic record
Abstract
PURPOSE: To report the occurrence, management, and outcome of late-onset traumatic dehiscence and dislocation of laser in situ keratomileusis (LASIK) flaps. METHODS: Two interventional case reports of patients with late-onset LASIK corneal flap dislocation after ocular trauma occurring at 7 and 26 months after surgery, respectively. RESULTS: The flaps were lifted, stretched, and repositioned after irrigation and scraping of the stromal bed and the underside of the flap. A bandage contact lens was placed, and topical antibiotic and corticosteroids were given postoperatively. The dislocated corneal flaps were successfully repositioned in both cases. The patient whose dislocated flap was repositioned 4 hours after the trauma recovered his uncorrected visual acuity (UCVA) of 20/20 1 week after the procedure and had a well-positioned flap with a clear interface. The patient who was managed 48 hours after the injury required repeat flap repositioning at 10 and 24 days after the initial procedure for treatment of persistent folds and striae in the visual axis. His uncorrected visual acuity 2 weeks after the third flap repositioning was 20/40 + 2. Diffuse lamellar keratitis developed in both patients that resolved with the use of topical corticosteroids. CONCLUSION: Laser in situ keratomileusis corneal flaps are vulnerable to traumatic dehiscence and dislocation, which can occur more than 2 years after the procedure.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".