Managing conflicts of interest in pharmacy and therapeutics committees: A proposal for multicentre formulary development
Bibliographic record
Abstract
WHAT IS KNOWN AND OBJECTIVE: While many countries have central agencies responsible for formulary development, within the United States, each hospital, health care system, or insurance provider has their own pharmacy and therapeutic committee, leading to both inefficiencies and inequalities across formularies. The number and variety of processes within pharmacy and therapeutic committees also increases the likelihood that conflicts of interest will influence the development of formularies. We sought to determine how such influences could be reduced by reviewing international evidence related to the presence and harms of conflicts of interest in formulary development. METHODS: Several approaches have been taken to reduce the influence of conflicts of interest in pharmacy and therapeutics committee processes, including include disclosure, recusal, exclusion, universal consideration and dual committees. The feasibility of each of these approaches is considered in the context of the United States. RESULTS AND DISCUSSION: A proposal is drawn from the discussion of various approaches to conflicts of interest in pharmacy and therapeutics committees: multicenter formulary development. WHAT IS NEW AND CONCLUSION: Multicentre formulary development, where resources are pooled across institutions, may lead to a reduction in the influence of conflicts of interest in pharmacy and therapeutics committee processes in the United States, increasing the chances of including the most safe, efficacious and cost-effective drugs on formularies.
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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 | 0.181 | 0.153 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".