Scientific Evidence Production and Specialty Drug Diffusion
Bibliographic record
Abstract
Specialty drugs treat complex, severe diseases and offer significant therapeutic advances. Despite their potential to transform patient care, little is known about the drivers of their diffusion. In this study, the authors develop a framework for the diffusion of specialty drugs that is motivated by the drugs’ novelty, complexity, and importance, with a focus on the role of scientific evidence production. They propose that the effects of scientific evidence on specialty drug diffusion are multifaceted and generated through the three stages of the scientific evidence production process: unpublished clinical studies, publications in medical journals not cited in clinical guidelines, and clinical guidelines. The findings from the empirical analysis of two specialty drugs validate the framework, supporting the idea of multistage scientific evidence effects. In contrast, marketing activities do not have a significant effect. Although this could be attributed to specialty drug prescribers discounting information from commercial sources, it may also be due to limited marketing support. An additional analysis on a nonspecialty drug further validates the proposed framework. Calculations of scientific evidence contributions to trial prescriptions indicate that scientific evidence production can generate returns beyond the publication stage, which should provide specialty drug manufacturers with strong incentives to commit to quality and innovation.
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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 | 0.109 | 0.377 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".