Risk Factors for Clavicle Refracture after Plate Removal: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Although uncommon, clavicle refracture following plate removal poses a major clinical concern. Identifying risk factors may help guide decisions on patient selection for plate removal. The study aims to systematically review and meta-analyze the available literature on risk factors associated with clavicle refracture after plate removal. We conducted a search in PubMed, Scopus, and ScienceDirect and included studies that compared patients with and without refracture after clavicle plate removal. Risk factors were pooled using odds ratios (OR) and standardized mean differences (SMDs), with a random or fixed-effects model depending on heterogeneity. The Newcastle-Ottawa Scale was used to appraise the quality of studies. Five retrospective studies were included, comprising a total of 1,135 patients who met the inclusion criteria. We found that female gender (OR: 0.30; 95% CI: 0.18–0.51) was associated with a lower observed refracture incidence, although this finding is likely confounded. In contrast, lower body weight SMD: 0.65; 95% CI: 0.28–1.03), smaller clavicle diameter (SMD: 0.57; 95% CI: 0.15–0.98), and shorter clavicle length (SMD: 0.68; 95% CI: 0.26–1.09) were significantly associated with an increased risk of refracture. This is the first meta-analysis to identify risk factors for clavicle refracture following plate removal. These findings may inform cautious risk–benefit discussions but do not support individualized prognostication at this time. Overall, the certainty is low, and these results should be interpreted as hypotheses to be tested in future studies, rather than as definitive predictors to guide clinical decision-making.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.003 | 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".