Monitoring adherence rates in glaucoma patients using the Travatan Dosing Aid. A 6-month study comparing patients on travoprost 0.004% and patients on travoprost 0.004%/timolol 0.5% fixed combination
Bibliographic record
Abstract
OBJECTIVE: To assess adherence in glaucoma patients using the Travatan Dosing Aid (TDA); to record differences in adherence by age, sex, therapy, systemic therapies, years from diagnosis, type of therapy and intraocular pressure (IOP). RESEARCH DESIGN AND METHODS: Sixth-month cohort study; fifty-six Caucasian, primary open-angle glaucoma patients on travoprost (T) or travoprost/timolol fixed combination (TTFC) monotherapy were submitted to four visits: at baseline and months 1, 3 and 6 (M1, M3, M6). Adherence was recorded with TDA and classified as 'high' if greater than 90%. Self-reported and physician-presumed adherence data were collected. Kruskall-Wallis and Fisher's exact tests were applied. RESULTS: Thirty-two patients (54.2%) were treated with T. Age, sex, level of schooling, presence of systemic comorbidities, duration of current therapy and IOP were similar between T and TTFC. Seventeen subjects (30.3%) recorded high adherence at every visit, 13 (23.2%) at two visits, 26 (46.4%) otherwise. Adherence was maintained over time with a slight decrease from month 1 to month 6 without statistical differences within and between groups. Adherence was statistically influenced by age (p = 0.007) and duration of therapy (p = 0.004). CONCLUSION: The typical nonadherent patient is elderly. TDA records indicate that only a minority of patients are really adherent: predictive models to screen for poor adherence are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".