Laser in situ keratomileusis buttonhole: Classification and management algorithm
Bibliographic record
Abstract
PURPOSE: To report the classification, management, and visual outcomes after laser in situ keratomileusis (LASIK) flap buttonhole caused by a microkeratome cut. SETTING: Private practice, Boston, Massachusetts, USA. METHODS: This retrospective observational case series comprised 15 patients with an intraoperative LASIK flap buttonhole or near buttonhole. In all cases, the flap was left in place or repositioned without excimer laser treatment. Buttonholes were classified by stage, and a treatment algorithm based on the stage was devised to determine the timing and type of intervention. The uncorrected visual acuity (UCVA), best spectacle-corrected visual acuity (BSCVA), and complications associated with the laser vision correction surgery were reported. RESULTS: Postoperative follow-up ranged from 1 week to 23 months. All 9 patients who were retreated had a postoperative UCVA of 20/25 or better. No retreated patient lost BSCVA. Before retreatment, the median UCVA was 20/80 (range 20/40(-1) to counting fingers), the median BSCVA was 20/20(-2) (range 20/15(-1) to 20/70), and the spherical equivalent (SE) refractive errors ranged from -1.00 to -6.62 diopters (D). After retreatment, the median UCVA was 20/20(-2) (range 20/15(-1) to 20/25(-1)), the median BSCVA was 20/20 (range 20/15 to 20/20(-3)), and the SE refractive errors ranged from +0.50 to -0.75 D. Complications after laser correction treatment included overcorrection in 3 patients and corneal haze in 2 patients. CONCLUSIONS: Classification of buttonholes was helpful in guiding treatment. Good UCVA and BSCVA were achieved by following a simple treatment algorithm based on surface ablation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".