Editorial: Shaping with data: Using pharmacoepidemiology to shape pharmaceutical policy and clinical decision-making
Bibliographic record
Abstract
Shaping with data: Using pharmacoepidemiology to shape pharmaceutical policy and clinical decision-makingPolicymakers around the world are grappling with an influx of rapidly changing science and new treatments in the area of medications and health technology.Much of their decisions have historically relied on randomized controlled trial (RCT) data for drug assessments.Over the past decade, it has become important for many decision-makers to realize that the RCT design may not reflect real-life clinical practice as the trial populations may exclude important patients seen in clinical practice.More importantly, the RCT is unable to answer questions related to rare adverse events, optimal use, and access.Therefore, the use of real-world data in the field of pharmacoepidemiology has, in many cases, stepped up to help policymakers fill these gaps.In this special topic, we aimed to gather a global cross-section of various papers that showcase the power of pharmacoepidemiology in helping shape policy and clinical practice.We set out to cover the many areas that pharmacoepidemiology can be used to shape and inform policymakers, including understanding beneficial and adverse drug effects of medications, drug utilization, real-world effectiveness, clinical effects of drug-drug interactions, effects of medication non-adherence, and the impact of policy changes on drug utilization.We were able to do just that and covered many of these topic areas.Excitingly we received work from over eight different countries, each leveraging unique data sources and study designs that truly highlight the breadth of work that can happen in this field.Most importantly we received work that showcased the ability for pharmacoepidemiology to be used in studying drug safety, policy, and clinically relevant questions.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".