Antiretroviral Medication Adherence and Persistence with Respect to Adherence Tool Usage
Bibliographic record
Abstract
Adherence to complex drug regimens over an extended period is a key factor in reaping the health benefits of highly active antiretroviral therapy (HAART). Forgetting a dose is the most commonly stated reason for suboptimal adherence, indicating a potential benefit of reminder devices. We examined antiretroviral drug adherence and duration of therapy with respect to adherence tool usage. Adherence was monitored for 12 months in a cohort of patients, using pharmacy refill data. Seventy-eight subjects were administered a questionnaire with regard to tool use at least once at 6 and/or 12 months; patients who replied to the questionnaire were eligible for this study. Persistence of remaining on therapy was obtained from the subjects, charts. The tools included individualized schedules, dosettes and electronic reminder devices, which were offered free of charge to all patients. Of the 64 subjects who entered this study, 60.9% (n = 39) used at least one adherence tool. The median adherence in those using tools was 95%; three quarters showed greater than 91% adherence. Adherence rates with respect to individual tools did not differ significantly for schedules and dosettes, with medians of 95% (n = 31) and 94% (n = 13), respectively. Median adherence with electronic reminders was 76% (n = 5). Seventy-four percent of patients remained on therapy after 12 months of study. Taking into consideration previous antiretroviral treatment, actual persistence at 12 months was 87%. Employing and individualizing strategies, including adherence tools, to enhance patient adherence to complex regimens in addition to counseling and follow-up, has resulted in good adherence rates and persistence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".