Optimizing therapeutic decision‐making for off‐label medicines use: A scoping review and consensus recommendations for improving practice and research<sup>+</sup>
Bibliographic record
Abstract
PURPOSE: Off-label medicines use is a common and sometimes necessary practice in many populations, with important clinical, ethical and financial consequences, including potential unintended harm or lack of effectiveness. No internationally recognized guidelines exist to aid decision-makers in applying research evidence to inform off-label medicines use. We aimed to critically evaluate current evidence informing decision-making for off-label use and to develop consensus recommendations to improve future practice and research. METHODS: We conducted a scoping review to summarize the literature on available off-label use guidance, including types, extent and scientific rigor of evidence incorporated. Findings informed the development of consensus recommendations by an international multidisciplinary Expert Panel using a modified Delphi process. Our target audience includes clinicians, patients and caregivers, researchers, regulators, sponsors, health technology assessment bodies, payers and policy makers. RESULTS: We found 31 published guidance documents on therapeutic decision-making for off-label use. Of 20 guidances with general recommendations, only 35% detailed the types and quality of evidence needed and the processes for its evaluation to reach sound, ethical decisions about appropriate use. There was no globally recognized guidance. To optimize future therapeutic decision-making, we recommend: (1) seeking rigorous scientific evidence; (2) utilizing diverse expertise in evidence evaluation and synthesis; (3) using rigorous processes to formulate recommendations for appropriate use; (4) linking off-label use with timely conduct of clinically meaningful research (including real-world evidence) to address knowledge gaps quickly; and (5) fostering partnerships between clinical decision-makers, researchers, regulators, policy makers, and sponsors to facilitate cohesive implementation and evaluation of these recommendations. CONCLUSIONS: We provide comprehensive consensus recommendations to optimize therapeutic decision-making for off-label medicines use and concurrently drive clinically relevant research. Successful implementation requires appropriate funding and infrastructure support to engage necessary stakeholders and foster relevant partnerships, representing significant challenges that policy makers must urgently address.
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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 | 0.376 | 0.540 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".