Incidence, possible risk factors, and potential effects of an opaque bubble layer created by a femtosecond laser
Bibliographic record
Abstract
PURPOSE: To describe the incidence, characteristics, risk factors, and sequelae of an opaque bubble layer created by the IntraLase (15 Khz) femtosecond laser (IntraLase, Corp.). SETTING: Private laser center and the Department of Ophthalmology, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada. METHODS: This study comprised 79 consecutive patients (149 eyes) who had laser in situ keratomileusis for myopic astigmatism. The preoperative visual acuity, refraction, keratometry, pachymetry, and intraoperative data including flap size and thickness were documented. A computerized system was used to calculate the total area of the opaque bubble layer. RESULTS: Eighty-four eyes (56.4%) developed an opaque bubble layer. The layer pattern was diffuse in 32.2% of eyes and hard in 24.2%. The diffuse opaque bubble layer covered a mean of 13.4%+/-10% of the corneal flap and the hard opaque bubble layer, a mean of 21.6%+/-10% (P= .0004). A significant correlation was noted between the corneal steep curvature and central corneal thickness (CCT) and the area of opaque bubble layer. Multivariate logistic regression found that flap diameter (P= .04) and CCT (P = .045) affected the occurrence and area of the opaque bubble layer (P= .04 and P= .05, respectively). Postoperative diffuse lamellar keratitis was not associated with an opaque bubble layer. Three months postoperatively, visual acuity and refraction were not affected by the bubble layer. There was an increase in trefoil aberrations in eyes with a hard opaque bubble layer (P= .01). CONCLUSIONS: Thicker corneas and smaller flaps were associated with a more opaque bubble layer. The presence of an opaque bubble layer did not seem to have detrimental long-term sequelae, although a small harmful effect could not be ruled out.
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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 | 0.003 |
| 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".