Bibliographic record
Abstract
BACKGROUND: Biomedical journals communicate new information that changes health-care decisions. If authors ignore the fundamental values of honesty and trust, that information becomes flawed, and society or patients may be harmed. OBJECTIVE: By describing two cases of unethical behaviour by authors, and using them as a focus to review acceptable ethics in publication, this article aims to educate readers who have not considered the ethical implications in writing manuscripts for biomedical journals. METHODS: Two cases of unethical behaviour by authors occurred when the results of new drug trials were reported. They were discovered after publication in a biomedical journal, and in the review process after the submission of a manuscript for publication respectively. In the first case, duplicate publication was identified because the same control data were used, but not acknowledged, in three publications by the same investigators. In the second, ghost writing by a pharmaceutical company writer was suspected because of the atypical presentation of a senior author's work. RESULTS: The editor consulted with the authors of both reports. In the first case, the authors concurred about the duplication, and the editors of the three journals wrote editorials to record the duplicate publications. The second case of ghost writing was unconfirmed by the authors, but the submission was withdrawn, and the article was later published in another journal. CONCLUSION: These cases draw attention to recently recognized types of scientific misconduct that influence the perception of scientific work. Duplicate publication and ghost writing not only deceive the reader, but may also conceal flawed study design and conflict of interest.
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 | 0.168 | 0.494 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.032 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.018 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".