Determinants of Adherence to Glaucoma Medical Therapy in a Long-term Patient Population
Bibliographic record
Abstract
PURPOSE: Estimate patient adherence to glaucoma medications and identify potential determinants of nonadherence. DESIGN: Descriptive study. METHODS: Two hundred patients with open angle glaucoma, ocular hypertension, or glaucoma suspects were interviewed regarding their glaucoma and its treatment and their charts were reviewed. Their ophthalmologist completed a brief assessment form. Drug utilization data were extracted from the provincial drug program database. Patients were defined as adherent if they filled at least 75% of the prescribed medication necessary for their treatment. RESULTS: Data were available for 181 patients. About 62.9% were female and the mean age (+/-SD) was 75.1+/-8.8 years. The mean number of years of glaucoma treatment was 10.7+/-9.3. Self-reported treatment adherence was 88.3%. On the basis of the drug database, the proportion of patients who were adherent to treatment was 71.8%. According to physicians, 74.6% of patients were adherent. Among patients considered by physicians as nonadherent, 71.1% (32/45) were adherent and among patients predicted as adherent, 72.1% (98/136) were adherent. There was no significant difference in adherence according to age, sex, education, and income. However, patients using fewer agents (P=0.041), who were widowed (P=0.041), or who lived alone (P=0.042) were more adherent. Patients using prostaglandins analogs or beta-blockers were more adherent than those using carbonic anhydrase inhibitors (P<0.05). CONCLUSIONS: Fewer medications, use of prostaglandin analogs or beta-blockers, living alone, and being widowed were significantly associated with adherence. Physicians were unable to significantly predict which patients are adherent.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".