Causes for Retraction in the Biomedical Literature: A Systematic Review of Studies of Retraction Notices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Many studies have evaluated the prevalence of different reasons for retraction in samples of retraction notices.We aimed to perform a systematic review of such empirical studies of retraction causes.Methods: The PubMed/MEDLINE database and the Embase database were searched in June 2023.Eligible studies were those containing sufficient data on the reasons for retraction across samples of examined retracted notices.Results: A 11,181 potentially eligible items were identified, and 43 studies of retractions were included in this systematic review.Studies limited to retraction notices of a specific subspecialty or country, journal/publication type are emerging since 2015.We noticed that the reasons for retraction are becoming more specific and more diverse.In a meta-analysis of 17 studies focused on different subspecialties, misconduct was responsible for 60% (95% confidence interval [CI], 53-67%) of all retractions while error and publication issues
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.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Meta-epidemiology (broad)Research integrity Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.075 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it