Effect of tobacco smoking on outcomes of trabeculectomy
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of tobacco smoking on trabeculectomy outcomes. METHODS: Charts of patients with glaucoma who underwent trabeculectomy performed by a single surgeon between 2007 and 2016 were retrospectively reviewed. Charts were screened for a documented history of smoking status before surgery. Demographic and clinical preoperative variables were recorded. Based on smoking history, subjects were divided into two groups: smokers and nonsmokers. Any bleb-related interventions (e.g., 5-flourouracil injections ± laser suture lysis) or bleb revision performed during the postoperative period were noted. Success was defined as an intraocular pressure >5 mmHg and <21 mm Hg without (complete success) or with (qualified success) the use of ocular hypotensive medications. Failure was identified as a violation of the criteria mentioned above. RESULTS: A total of 98 eyes from 83 subjects were included. The mean age of the subjects was 70.7 ± 11.09 years, and 53% (44/83) were female. The most common diagnosis was primary open-angle glaucoma in 47 cases (47.9%). The smokers Group included 30 eyes from 30 subjects. When compared with nonsmokers, smokers had a significantly worse preoperative best-corrected visual acuity (p=0.038), greater central corneal thickness (p=0.047), and higher preoperative intraocular pressure (p=0.011). The success rate of trabeculectomy surgery at 1 year was 56.7% in the smokers Group compared with 79.4% in the Group nonsmokers (p=0.020). Smoking presented an odds ratio for failure of 2.95 (95% confidence interval, 1.6-7.84). CONCLUSION: Smokers demonstrated a significantly lower success rate 1 year after trabeculectomy compared with nonsmokers and a higher requirement for bleb-related interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".