Pill Burden in HIV Infection: 20 Years of Experience
Bibliographic record
Abstract
BACKGROUND: New antiretroviral (ARV) drugs and improved formulations and coformulations of existing ARVs are actively promoted to diminish a patient's pill burden and to minimise the opportunity for mismatched dosing, although the effect of these advances is poorly understood. We determine how these changes affect the total daily pill burden (TDPB) for ARV and other drug use over a 20-year period. METHODS: Using our in-house pharmacy database, we calculated the daily number and associated pill burden of oral ARV and non-ARV (prescription and over-the-counter) medications taken by every patient within the Southern Alberta Cohort (SAC) between 1990 and 2010. We compared the mean TDPB with the patients' sociodemographic and clinical characteristics. RESULTS: Mean TDPB for ARV-experienced patients increased from 4.9 in 1990 to 12.1 in 1998 but decreased to 6.7 in 2010. By 2010, new ARVs and newer formulations had decreased ARV pill burden by 50% over five years. In 1990, however, 95% of the TDPB consisted of ARVs but by 2010 it fell to 51% as non-ARV daily drugs increased from 0.2 in 1990 to 2.6 in 2010. Variation in TDPB was attributable to four main factors: patient's age, risk factor, nadir CD4(+) T-cell count and duration of known HIV infection. CONCLUSIONS: While new ARV formulations and coformulations have simplified regimens, this reduction in ARV pill burden has been counterbalanced by increases in non-ARV drugs required for managing comorbidities. Discussions on merits of coformulations in decreasing ARV pill burden need to include the non-ARV pill burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".