Post-traumatic Osteoarthritis of the Elbow Fractures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Elbow fractures are recognized as a cause of post-traumatic osteoarthritis (PTOA) of the elbow, and there are wide variations in the studated incidences. The incidence and risk factors for developing PTOA after elbow fractures are reviewed in this systematic review and meta-analysis. Methods: We searched PubMed, Embase, Web of Science, Cochrane Library, and Scopus from inception to February 2024, and conducted a systematic review and meta-analysis. Elbow fractures in adults with at least 12 months’ followup were included in studies reporting incidence and risk factors for PTOA. For study and patient characteristics, fracture classification, treatment, and incidence of PTOA, data extraction was performed. Newcastle Ottawa Scale was used to assess quality. Heterogeneity was addressed by random-effects meta-analysis and subgroup/meta-regression analyses. Results: The 25 studies included involved a total of 1,538 patients. The pooled incidence of PTOA after elbow fractures was 30.3% (95% CI: 25.2%–35.8%). Incidence of simple fractures and intra articular fractures was reported. Significant risk factors included advanced age, male gender, fracture displacement and comminution, intraarticular fracture fragments, and nonoperative treatment. Conclusion: In approximately 30% of patients who have an elbow fracture, PTOA will occur, and intra-articular fractures are associated with a higher risk. Optimization of management and reduction in long term arthritis may be achieved by identifying modifiable risk factors (eg, ensuring fracture reduction and fixation).
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".