A systematic review of retracted publications in emergency medicine
Bibliographic record
Abstract
The objective of this study was to characterize retracted publications in emergency medicine. We searched MEDLINE, Web of Science and Cochrane Central Register of Controlled Trials to identify all retracted publications in the field of emergency medicine. We also searched an independent website that reports and archives retracted scientific publications. Two researchers independently screened titles, abstracts and full text of search results. Data from all included studies were then independently extracted. We identified 28 retraction notes. Eleven (39%) articles were published by authors from Europe. The oldest retracted article was published in 2001. The 28 retracted papers were published by 22 different journals. Two authors were named on multiples retractions. The median impact factor of journals was 1.03 (0.6-1.9). Almost all studies were available online [26/28 (93%)], but only 40% had watermarking on the article. The retraction notification was available for all articles. Three (11%) retraction notices did not clearly report the retraction reasons, and most retraction notices were issued by the editors [14 (56%)]. The most frequent retraction reasons were plagiarism [eight (29%)], duplicate publication [three (11%)] and overlap [two (2%)]. Retracted articles were cited on average 14 times. In most cases, the retraction cause did not invalidate the study's results [17 (60%)]. The most common reason for retraction was related to a misconduct by the authors. These results can question the necessity to normalize retraction procedures among the large number of biomedical editors and to educate future researchers on research integrity.
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 | 0.044 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.034 | 0.028 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".