Risk Factors for a Severe Bleb Leak Following Trabeculectomy
Bibliographic record
Abstract
PURPOSE: To describe the population at risk of having a severe bleb leak needing a surgical repair in the operating room and to study risk factors associated with severe bleb leak. PATIENTS AND METHODS: In this case-control study, 17 cases were enrolled and paired with 51 controls. We studied all patients having a surgical revision in our center for a severe bleb leak between January 1 and December 31, 2008. Three controls were paired to each case based on their surgery date. We then analyzed risk factors related to the patient, the intervention, and the postoperative follow-up. RESULTS: Younger age was the only statistically significant risk factor for a severe bleb leak in our study. The odds of a severe bleb leak decreased as the age increased (P=0.0029). In comparing the risk for a severe bleb leak in younger (below 55 y) versus patients aged 75 years or older, the odds ratio was 21.0. There were no statistically significant differences between cases and controls with respect to: type of glaucoma, number or types of previous ocular surgeries, number of preoperative topical medications, localization of the leak, localization of the wound (fornix or limbus-based), or the intraocular pressure on day 1 postoperative. CONCLUSIONS: Younger age at the time of trabeculectomy may be a risk factor for severe bleb leak. A trend was observed in which the patients under the age of 55 years were at greater risk for a severe bleb leak.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".