A89 CHARACTERISTICS AND CONFLICTS OF INTEREST AT FOOD AND DRUG ADMINISTRATION GASTROINTESTINAL DRUG ADVISORY COMMITTEE MEETINGS
Bibliographic record
Abstract
Abstract Background The United States Food and Drug Administration (FDA) Gastrointestinal Drug Advisory Committee (GIDAC) is involved in gastrointestinal drug application reviews. Characteristics and conflicts of interest (COI) in GIDAC meetings are not well described. Aims To analyze FDA GIDAC meetings and characteristics that predict recommendations. Methods This was a cross-sectional study of all publicly available GIDAC meetings where proposed medications were voted on from 1998–2018. Data were collected on individual meetings and individual voting members at meetings. Predefined predictor variables included type of medication, medication sponsor, primary efficacy studies, and voting member characteristics (e.g. committee membership, COI). Univariate analyses were conducted at per-meeting and per-vote levels to assess for predictors of committee recommendation and individual votes respectively. Results Thirty-four meetings with 476 individual votes from 1998–2018 were included. Twenty-three (68%) proposals were recommended for approval and 25 (74%) received FDA approval. Most proposals involved >1 primary study (n=27, 79%). At least one voting member had a COI in 24 (71%) of 34 meetings. Twelve (35%) meetings had at least one sponsor COI. Among 476 individual votes, 74 (15.5%) involved a COI, with 33 (6.9%) sponsor COI. COI decreased significantly over time, with more COI in 1996–2000 and 2001–2005 compared to 2006–2010, 2011–2015, and 2016–2020 (p<0.01). There were no significant associations between pre-defined predictors, including COI, and committee level recommendations or individual votes (p>0.05 for all univariate analyses). Conclusions The GIDAC reviewed 34 proposals from 1998–2018. The majority were recommended for approval and later approved by the FDA, highlighting the GIDAC’s prominence in the regulatory process. COI are present among GIDAC panelists but decreasing over time and not associated with recommendations. Funding Agencies None
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".