Long-Term Patient Adherence to Antiretroviral Therapy
Bibliographic record
Abstract
OBJECTIVE: To measure patient adherence to antiretroviral therapy over a two-year period and to identify factors impacting adherence. METHODS: In a regional HIV treatment center, 100 consecutive patients starting any new antiretroviral agent were enrolled in this study, which consisted of a one-year retrospective data review and a one-year prospective component. The tools used for evaluating adherence were the monthly prescription refill data and a patient questionnaire. Data analyzed included overall adherence, adherence to individual antiretrovirals, and change in adherence over time, as well as factors reported as influencing adherence. RESULTS: Greater than 80% adherence in taking prescribed doses was seen in 75% of patients during the retrospective phase of the study; adherence increased to 84% in the prospective phase. Throughout the prospective phase of the study, monthly median adherence rates were 98-100%. Suboptimal adherence secondary to pill fatigue or number of daily pills did not occur. Reported nonadherence to dietary restrictions varied among drugs. The primary cause given for poor adherence was difficulty remembering followed by inconvenient dosing schedule and difficulty scheduling administration times around meals. At least one adherence tool was used by 61% of patients. A diagnosis of AIDS was associated with lower adherence in our patient population (p = 0.039); substance abuse and psychiatric history had no influence. CONCLUSIONS: Adherence to antiretroviral treatment regimens did not diminish over the two years studied. Several patients with poor adherence were identified, emphasizing the importance of addressing this issue both prior to and throughout treatment. A personalized approach by healthcare providers can optimize patient adherence to antiretroviral therapy by providing careful drug selection in addition to routine follow-up and the provision of information, feedback, and reminder systems.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".