A104 PREVALENCE OF GHOST-AUTHORSHIP IN INDUSTRY-SPONSORED CLINICAL TRIALS
Bibliographic record
Abstract
Abstract Background Ghost-authorship involves the exclusion of individuals who have made substantial contributions to the article from the author byline. Previous studies have found that ghost-authorship is highly prevalent in industry-sponsored clinical trials. Its prevalence, however, has yet to be investigated in trials of biologics in the management of inflammatory bowel disease (IBD). Aims To determine the prevalence of ghost-authorship in IBD biologic industry-sponsored clinical randomized controlled trials (RCTs). Methods Biologic medications indicated for ulcerative colitis (UC) or for Crohn’s disease (CD) were identified using the Food and Drug Agency (FDA) database. We identified the clinical trials on clinicaltrials.gov corresponding to the data presented at the time of FDA approval. Specifically, we included the first publication for each trial to report study results for our analysis. Two authors independently identified the presence of ghost-authorship, which we defined as the exclusion on the author byline of the included RCT publication of any individuals who assisted in the writing of the trial manuscript and/or performed the data analyses. Results We identified a total of 28 relevant RCTs on biologic medications (10 for UC and 18 for CD), which were matched to 20 publications. We found ghost-authorship in 70% of publications (n=14); 40% (n=8) involved manuscript and protocol writing assistance from sponsor staff; 35% (n=7) involved medical writers from external companies; 15% (n=3) involved both sponsor staff and medical writers assisting in manuscript writing; and 20% (n=4) involved individuals performing data analysis or interpretation. Conclusions We found that ghost-authorship in industry-sponsored IBD biologic clinical trials has a moderately high prevalence, with the most common being manuscript or protocol writing assistance. A lack of transparency regarding sponsor-affiliated and/or external contributors may negatively affect the trust placed in medical research. One limitation is that data was only extracted from publications. Further evidence on ghost-authorship may be found in study protocols and registrations, which will be investigated in the future. 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 | 0.275 | 0.664 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".