Publication fraud: implications to the individual and to the specialty
Bibliographic record
Abstract
PURPOSE OF REVIEW: To provide a brief review and update on the subject of scientific misconduct relevant to the specialty of anesthesia. The overall goal is to raise awareness amongst readers of the scientific literature that although publication fraud is relatively infrequent, the reasons for fraud are complex and the consequences to the individual and for the specialty are substantial. RECENT FINDINGS: Scientific misconduct and publication fraud can easily go undetected. However, plagiarism is being detected with increasing frequency as a result of newer detection software. There are recent examples of scientific misconduct involving extensive data fabrication in the anesthesiology literature that have far-reaching, and as-yet-to-be determined implications for the scientific record. The reasons for publication fraud and methods to detect scientific misconduct are reviewed. The implications for related studies in the field, systematic reviews and practice guidelines are considered. SUMMARY: When suspected, alleged misconduct must be thoroughly investigated. When proven, scientific misconduct must be addressed by the relevant institutions and the scientific record must be corrected. Many stakeholders are involved in this complex and most unfortunate process.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".