The Presence of Ghost Publications Among Canadian Plastic Surgery Residency Applicants: How Honest Are Canadians?
Bibliographic record
Abstract
Background: Physicians with history of unprofessional behaviour during their medical training are shown to be 3 times more likely to have board disciplinary action later in their career. One realm in which unprofessional behaviour takes place is the phenomenon of unverifiable publications or "ghost publications." To that end, this study aims to assess the rate of ghost publications among a recent cohort of Canadian Plastic Surgery residency applicants to determine if this phenomenon is geographic in nature. Methods: The current study was a retrospective, cross-sectional observational study; a review of all residency applications submitted to a single Canadian Plastic Surgery residency program from 2015 to 2018 was performed and all their listed publications were verified for accuracy. The review was conducted by a third party librarian and a research coordinator blinded to the authors identifying information. "Ghost publication" was defined as any publication listed as "published," "accepted," or "in-press" that did not exist in the literature. Results: A total of 196 applications of 186 applicants were submitted over the span of 4 years. A total of 362 publications listed as peer-reviewed articles, belonging to 114 applications were extracted and reviewed. Among the 362 publications listed as peer-reviewed articles, 2 could not be found in the literature (0.55%). Additionally, 42 citations were found with 48 minor differences than what was cited. Conclusions: The rate of ghost publications among recent applicants to a Plastic Surgery residency program is low (less than 1%). Future studies should investigate methods to further improve and instill the value of professionalism in our future plastic surgery trainees.
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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 | 0.010 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".