De‐simplifying single‐tablet antiretroviral treatments: uptake, risks and cost savings
Bibliographic record
Abstract
OBJECTIVES: As more HIV-positive individuals receive antiretroviral therapy (ART), payers are seeking options for covering these increased and sustained drug costs. Strategic use of available generic antiretroviral (ARV) formulations may be feasible. De-simplifying a single-tablet co-formulation (STF) into two or more tablets using both brand and generic drugs has been proposed. We determine if voluntary de-simplification of one STF could be utilized as a cost-saving strategy. We report on the challenges, uptake, outcomes and cost savings of this initiative. METHODS: . No incentives were provided. We examined the acceptance/decline rates, patient satisfaction, health care outcomes and annual cost savings. RESULTS: , 321 were approached; 177 (55.1%) agreed to de-simplify. Of patients initiating ART, 62.7% chose the generic co-formulation. Patients switching to or starting on the generic co-formulation were more likely to be male, > 45 years old, Caucasian, men who have sex with men (MSM) and more HIV-experienced, and to have more comorbidities (all P < 0.05). Preference for STF was cited for declining de-simplification. No concern about generic ARVs was expressed. The rate of viral load > 500 HIV-1 RNA copies/mL after baseline was 2.7% in switched patients compared with 7.0% in those declining to switch. No de novo resistance occurred. A saving of Cdn$1 319 686 was achieved in the first year. CONCLUSIONS: Reliance on altruism, while respecting patient autonomy, achieved de-simplification in > 50% of patients approached, and generated immediate cost savings with no increased risk of adverse events, viral breakthrough or resistance.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".