Association Between the Use of Glaucoma Medications and Mortality
Bibliographic record
Abstract
OBJECTIVE: To evaluate the relationship between glaucoma medication use and death. METHODS: This study uses longitudinal data from 2003 to 2007 on persons 40 years and older with glaucoma or suspected glaucoma enrolled in a large managed care network. Cox regression analysis was performed to estimate the hazard of death associated with the use of various glaucoma medication classes and combinations thereof. Multivariable models were adjusted for demographic characteristics and comorbid medical conditions. RESULTS: Of 21 506 participants with glaucoma or suspected glaucoma, 237 (1.1%) died during the study period. The use of any class of glaucoma medication was associated with a 74% reduced hazard of death (adjusted hazard ratio [HR], 0.26; 95% confidence interval [CI], 0.16-0.40) compared with no glaucoma medication use. This association was observed for use of a single agent alone, such as a topical beta-antagonist (0.44; 0.24-0.83) or a prostaglandin analogue (0.31; 0.18-0.54), and for use of different combinations of drug classes. CONCLUSIONS: After adjustment for potential confounding variables, the use of glaucoma medications was associated with a reduced likelihood of death in this large sample of US adults with glaucoma. Future investigations should explore this association further because these findings may have important clinical implications.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| 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 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".