Does candidate for plate fixation selection improve the functional outcome after midshaft clavicle fracture? A systematic review of 1348 patients
Bibliographic record
Abstract
INTRODUCTION: conservative) would give better functional outcome than random treatment allocation. METHODS: We performed a systematic literature search for primary studies providing functional score and non-union rate after conservative or surgical management of midshaft clavicle fractures. Six randomized controlled trial and 19 non-randomized controlled trial studies encompassing a total of 1348 patients were included. RESULTS: Patients treated with surgical management were found to have statistically superior Constant scores in non-randomized controlled trials than in randomized controlled trials (94.76 ± 6.4 versus 92.49 ± 6.2; p < 0.0001). For conservative treatment, randomized controlled trials were found to have significantly better functional outcome. The prevalence of non-union (6.1%) did not show significant statistical difference between non-randomized controlled trial and randomized controlled trial studies. The functional outcome after surgical management was significantly higher than after conservative management in both randomized controlled trial and non-randomized controlled trial groups. The non-union rate after surgery (1.1% for both non-randomized controlled trial and randomized controlled trial) was significantly lower than following conservative treatment (9.9% non-randomized controlled trial versus 15.1% randomized controlled trial). DISCUSSION: This review shows that patient selection for surgery may influence functional outcome after midshaft clavicle fracture. Our results also confirm that plate fixation provides better functional outcome and lower non-union rate.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".