Highly Active Antiretroviral Therapy: Physician Experience and Enhanced Adherence to Prescription Refill
Bibliographic record
Abstract
OBJECTIVE: To identify patient and physician characteristics that may act as determinants of adherence to prescription refill of triple combination antiretroviral therapy. METHODS: A population-based analysis of antiretroviral therapy-naive HIV-positive men and women in British Columbia, Canada, who initiated triple combination therapy between August 1 1996 and October 31 1998. Study participants were considered adherent if they were actually dispensed antiretrovirals > or = 95% over the first year of therapy. Log-binomial regression was used to identify patient and physician characteristics associated with adherence to prescription refill. RESULTS: Of the 886 individuals eligible for analysis, 495 (56%) were > or = 95% adherent to prescription refill. In multivariate analysis, adherence was positively associated with increased age [adjusted relative rate (ARR) 1.19; 95% CI: 1.07-1.32], having a diagnosis of AIDS (ARR 1.66; 95% CI: 1.29-2.15), being male (ARR 1.79; 95% CI: 1.27-2.53), and with greater experience of the treating physician (ARR 1.27; 95% CI: 1.13-1.42). History of injection drug use was negatively associated with adherence to prescription refill (ARR 0.65; 95% CI: 0.51-0.83), as was increased pill burden (per pill daily) (ARR 0.95; 95% CI: 0.92-0.99). A sub-analysis of 316 patients who provided additional data regarding psychosocial characteristics indicated that adherence was positively associated with physician experience (ARR: 1.28; 95% CI: 1.09-1.51) and being employed (ARR: 1.55; 95% CI: 1.14-2.21), and negatively associated with a history of injection drug use (ARR: 0.61; 95% CI: 0.43-0.85). CONCLUSION: While patient disease stage and personal characteristics may play an important role in patient adherence to prescription refill of complex therapeutic regimens, our findings indicate that HIV-experienced physicians may have greater success in maintaining patients on prescribed therapy.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".