Legal Remedies for Medical Ghostwriting: Imposing Fraud Liability on Guest Authors of Ghostwritten Articles
Bibliographic record
Abstract
Ghostwriting and guest authorship of medical journal articles raise serious ethical and legal concerns, bearing on the integrity of medical research and evidence used in legal disputes. Ghostwriting involves undisclosed authorship, usually by medical communications agencies or a pharmaceutical sponsor of the published research; guest authorship involves taking authorial credit for the published work without making a substantial contribution to it. Commentators have objected to these practices because of concerns involving bias in ghostwritten clinical trial reports and review articles. We also note the effects of ghostwritten articles on questions involving the legal admissibility of scientific evidence. Efforts to curb ghostwriting practices, undertaken by medical journals, academic institutions, and professional disciplinary bodies, have thus far had little success and show little promise. These organizations have had difficulty adopting and enforcing effective sanctions, for specific reasons relating to the interests and competencies of each kind of organization. Because of those shortcomings, a useful deterrent in curbing the practice may be achieved through the imposition of legal liability on the ‘guest authors’ who lend their names to ghostwritten articles. We explore the doctrinal grounds on which such articles might be characterized as fraudulent. A guest author’s claim for credit of an article written by someone else constitutes legal fraud, and may give rise to claims that could be pursued in a class action based on the Racketeer Influenced and Corrupt Organizations Act (RICO). The same fraud could support claims of “fraud on the court” against a pharmaceutical company that has used ghostwritten articles in litigation. This doctrine has been used by the U.S. Supreme Court to impose sanctions on the authors and corporate sponsors of a ghostwritten article. We discuss the potential penalties associated with each of these varieties of fraud.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.069 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.013 | 0.023 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.028 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".