Retractions in orthopaedic research
Bibliographic record
Abstract
OBJECTIVES: Despite the fact that research fraud and misconduct are under scrutiny in the field of orthopaedic research, little systematic work has been done to uncover and characterise the underlying reasons for academic retractions in this field. The purpose of this study was to determine the rate of retractions and identify the reasons for retracted publications in the orthopaedic literature. METHODS: Two reviewers independently searched MEDLINE, EMBASE, and the Cochrane Library (1995 to current) using MeSH keyword headings and the 'retracted' filter. We also searched an independent website that reports and archives retracted scientific publications (www.retractionwatch.com). Two reviewers independently extracted data including reason for retraction, study type, journal impact factor, and country of origin. RESULTS: One hundred and ten retracted studies were included for data extraction. The retracted studies were published in journals with impact factors ranging from 0.000 (discontinued journals) to 13.262. In the 20-year search window, only 25 papers were retracted in the first ten years, with the remaining 85 papers retracted in the most recent decade. The most common reasons for retraction were fraudulent data (29), plagiarism (25) and duplicate publication (20). Retracted articles have been cited up to 165 times (median 6; interquartile range 2 to 19). CONCLUSION: The rate of retractions in the orthopaedic literature is increasing, with the majority of retractions attributed to academic misconduct and fraud. Orthopaedic retractions originate from numerous journals and countries, indicating that misconduct issues are widespread. The results of this study highlight the need to address academic integrity when training the next generation of orthopaedic investigators.Cite this article: J. Yan, A. MacDonald, L-P. Baisi, N. Evaniew, M. Bhandari, M. Ghert. Retractions in orthopaedic research: A systematic review. Bone Joint Res 2016;5:263-268. DOI: 10.1302/2046-3758.56.BJR-2016-0047.
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
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: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | MetaresearchResearch integrity Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.194 | 0.611 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".