Polypharmacy in HIV: Impact of Data Source and Gender on Reported Drug Utilization
Bibliographic record
Abstract
Drug use in HIV is complex and may involve multiple therapeutic and nontherapeutic agents including prescription, over-the-counter, complementary and alternative medicine, and social/recreational drugs. This study was designed to assess the extent of such drug use in HIV-infected men and women. One hundred four adults were recruited through the HIV Ontario Observational Database from HIV outpatient clinics throughout Ontario, Canada. Patient demographics and data on drug use and physician awareness of drug use were collected through in-person interviews and medical chart review. All patient interviews and 96% of medical charts revealed the use of at least one drug. Eighty-five percent of patients reported use of antiretroviral medications; nearly 70% used highly active antiretroviral therapy. Patients used significantly more drugs by patient report (15.7 +/- 7.7) than by medical chart review (8.4 +/- 5.0) reporting up to 39 drugs per person. Pill burden was substantial, averaging 20.7 +/- 12.5 and ranged up to 69 "pills-per-day." Patient-reported physician awareness of drug use was highest for prescription drugs and lowest for social/recreational drugs; correspondingly agreement between medical chart and patient report ranged from 80% for antiretrovirals to 10% for non-prescribed drugs. The drug and pill burden faced by patients with HIV is considerable. Prevalence of use for specific drug classes varied with both data source and gender while number of drugs used differed only by data source. Our findings emphasize the complexity of pharmacotherapy in HIV and the need for comprehensive drug assessment, particularly because of the risks of drug-drug interactions and decreased adherence secondary to therapeutic complexity.
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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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".