Comprehensive Drug-Class Review Framework for improved evidence-based drug policy and formulary modernization
Bibliographic record
Abstract
Formularies are used by payers to optimize access and ensure the appropriate use of medications. Lack of follow-up and re-evaluation can lead to outdated formularies that are not reflective of current evidence. Formulary modernization, an approach to re-align formularies with current evidence has proven successful. The Ontario Drug Policy Research Network (ODPRN) launched a framework for conducting comprehensive drug-class reviews. This commentary describes the individual components of this framework and lessons learned through completion of 12 reviews between 2013 and 2016. We present the ODPRN drug-class review of treatments for chronic hepatitis B as a case example to illustrate the components and impact. The incorporation of foundational health technology assessment components such as economic evaluations and knowledge synthesis with contextualizing evidence such as patient and clinician perspectives (through qualitative studies), real-world evidence (through data analytics), and cross-jurisdictional comparisons (through environmental scans and data analytics), successfully developed jurisdictionally specific policy recommendations grounded in up-to-date evidence. The ODPRN framework for conducting comprehensive drug-class reviews is a robust and feasible approach to conduct formulary modernization. This framework allows for actionable and specific policies which are likely to be considered by decision makers. Adoption of similar frameworks in other jurisdictions may improve uptake of evidence-informed policy recommendations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.589 | 0.559 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.053 | 0.029 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.037 | 0.028 |
| Open science | 0.017 | 0.021 |
| Research integrity | 0.029 | 0.021 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".