The Relationship Between Sociodemographic Factors and Persistence With Topical Glaucoma Medications
Bibliographic record
Abstract
PURPOSE: To investigate the relationship between sociodemographic factors and nonpersistence with topical glaucoma medication. DESIGN: This was a retrospective, observational cohort study. PATIENTS AND METHODS: We invited glaucoma patients on medical therapy from a general ophthalmology practice to complete a standardized questionnaire between November 2011 and April 2012. Nonpersistence was defined as having ≥ 1 gaps (≥ 14 d without medication) in therapy over the last year. Patients' pharmacy records, dating back 1 year from study enrollment, were used to determine the total number of gaps and the cumulative number of days off therapy in the last year. Prevalence ratios (PR) and 95% confidence intervals (CI) were used to assess the relationship between sociodemographic factors and nonpersistence. The relationships between sociodemographic factors and the median number of gaps, as well as the median number of days off, were also assessed. RESULTS: Sixty-one patients were included for analysis. The mean age was 72 years; 61% were male patients and 71% were on one medication for glaucoma. Fifty-four percent of patients (n=33) were nonpersistent with glaucoma medications over the 1 year study period. Median numbers of gaps and days off therapy were 1 and 11, respectively. Patients reporting below average income were twice as likely to be nonpersistent (prevalence ratio, 2.02; 95% confidence interval, 1.37-2.96; P<0.01). Below average income also trended toward a greater median number of days off therapy (P=0.07). CONCLUSIONS: Below average socioeconomic status may negatively impact persistence with topical glaucoma medications, potentially threatening long-term visual outcomes.
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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 | 0.005 |
| 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".