Effect of Local Anesthesia on Trabeculectomy Success
Bibliographic record
Abstract
PURPOSE: To compare the long-term results of trabeculectomy surgery with subconjunctival anesthesia versus topical lidocaine 2% jelly. METHODS: A retrospective review of the long-term intraocular pressure (IOP) of 57 trabeculectomies previously enrolled in a prospective study comparing subconjunctival anesthesia to topical lidocaine 2% jelly. Baseline data included patient demographics, diagnosis, and ophthalmic history. Postoperative data included IOP, glaucoma therapy, and any interventions. Follow-up was conducted by reviewing the medical charts from July 2002 to August 2007. Differences between the groups were statistically assessed by the Student t test, chi(2) test, Fisher exact test, and Kaplan-Meier survival analysis. RESULTS: Data were available for 57 of the 58 original study patients, with a median age of 65 years. The median follow-up time was 4.2 years for both groups (range: 0.1 to 4.8). There were no statistically significant differences in baseline characteristics and follow-up observations. At the 4-year follow-up, 29.5% of the subconjunctival anesthesia patients versus 39.5% of the topical lidocaine 2% jelly patients were complete success (IOP between 6 to 21 mm Hg and 20% reduction without glaucoma therapy or repeat filtration surgery, P=0.15) and 82.7% of the subconjunctival anesthesia patients versus 95.8% for the topical lidocaine 2% jelly patients were qualified success (above with or without glaucoma therapy, P=0.39). CONCLUSIONS: Though small numbers observed, the 2 anesthetic techniques did not seem to influence the long-term success of trabeculectomy surgery. Further studies with more patients are warranted.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".