Preservative Exposure and Surgical Outcomes in Glaucoma Patients
Bibliographic record
Abstract
PURPOSE: To study the impact of benzalkonium chloride (BAK) exposure from eye drops on subsequent time to trabeculectomy failure. PATIENTS AND METHODS: Retrospective chart review of 128 glaucoma patients who had undergone a trabeculectomy between 2004 and 2006. The number and type of ophthalmic drops used preoperatively and relevant demographics were recorded. Surgical failure criteria included inadequate pressure lowering or need for postoperative ocular antihypertensives, laser trabeculoplasty, 5-fluorouracil needling, or repeated surgery. Patients were examined for these criteria over a minimum postoperative period of 2 years. Data were assessed using Kaplan-Meier and Cox regression models. RESULTS: Complete surgical success was achieved in 47.7% of patients. Patients received between 1 and 8 BAK-containing drops daily, with a median of 3. Time to surgical failure in patients receiving higher preoperative daily doses of BAK was shorter than in patients who had less BAK exposure (P=0.008). Proportional hazard modeling identified uveitic and neovascular glaucoma as significant confounders of the univariate model (P=0.024), although the main effect of BAK exposure was maintained with a hazard ratio of 1.21 (P=0.032). The number of different medications used to control intraocular pressure did not significantly affect survival time in a secondary Cox model (P=0.948). CONCLUSIONS: Increased preoperative exposure to ophthalmic solutions preserved with BAK is a risk factor for earlier surgical failure, independent of the number of medications used. This study extends earlier findings of potential adverse effects of ophthalmic preservatives on surgical outcomes to the modern pharmacopeia used in the medical management of glaucoma.
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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 | 0.001 |
| 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".