A Model for an Academia-Industry Collaboration for Pharmacovigilance and Pharmacoepidemiology
Bibliographic record
Abstract
Purpose We describe a novel model for collaboration between academia and the pharmaceutical industry, focused on post-marketing pharmacovigilance. Methods Otsuka Pharmaceutical, a global Japan-based pharmaceutical company, and Columbia University, a major university, have established a collaboration dedicated to working together in pharmacoepidemiology and post-marketing pharmacovigilance. An oversight committee, made up of individuals from each institution, meets on a regular basis to set policy and provide oversight. Results The primary aim of this novel academia-industry collaboration is to provide expert research guidance for the industry pharmacovigilance group on questions involving pharmacoepidemiology. University epidemiologists may also be consulted by other divisions of industry, such as the clinical trials group. The first aim of the collaboration is to provide epidemiologic input to industry by determining the incidence, prevalence, and outcomes of diseases; drafting the epidemiology components of risk management plans for drugs; and planning retrospective database analyses. A second major aim is to provide educational services to industry by conducting workshops on basic epidemiology and biostatistics; leading a monthly lecture/journal club series; hosting seminars on medical topics; and providing a writing workshop to assist in preparing abstracts and papers for presentation and publication. University epidemiologists provide oversight/evaluation through quarterly presentations and updates to the industry partner’s external advisory committee as well as to University leadership. Conclusions This unique academia-pharmaceutical industry collaboration enhances understanding of the medical and epidemiologic challenges faced within a pharmacovigilance department of a global pharmaceutical company. We offer it as a model for others performing mandatory regulatory post-marketing pharmacovigilance activities.
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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 | 0.043 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.021 | 0.026 |
| Open science | 0.005 | 0.025 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".