Ghostwriting in biomedicine: a review of the published literature
Bibliographic record
Abstract
Introduction: The systematic review of biomedical ghostwriting has proven challenging due to problems in consistency and in study design. Moreover, authorship guidelines established by the International Committee of Medical Journal Editors (ICMJE) may have inadvertently created opportunities to potentiate ghostwriting. Given continued interest in ghostwriting by the International Society of Medical Publication Professionals (ISMPP) and other organizations, we undertook an analysis of ghostwriting in the biomedical literature.Methods: We searched PubMed (search terms: ghost writ*, ghostwrit*, ghost writer, ghostwriter, ghostwriting and ghost writing). Results, including abstracts, were reviewed for relevance (relationship to ghostwriting in biomedical journals) to aid in removal of inapplicable work and duplicate publications. After review, we consolidated expert opinions for publication professionals.Results: Overlap was poor across search terms; of 181 unique papers identified, most (112/181) were opinion pieces. An increasing number of papers are using the term “ghostwriting” to describe genetics as well as diverse phenomena of misattributed authorship, including “ghost authorship”. Eight primary studies and 1 systematic review of ghostwriting incidence were identified, reporting prevalence ranging from <1% to 91%, in varied settings using differing methods and definitions of ghostwriting. Suggestions for avoiding ghostwriting include early consensus building and better definitions of authorship among manuscript teams.Discussion: The prevalence and definition of ghostwriting remain unclear. Increased transparency and auditable authorship practices that align with specific guidelines may aid in the avoidance of ghostwriting. In addition, MeSH or clearer indexing terms may be helpful to separate usages of ghostwriting in scientific settings (e.g. genetic research) versus biomedical publishing.
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 | 0.047 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.039 | 0.039 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".