Mechanism of Injury Affects the Incidence and Time to Recovery of Nerve Injuries Associated With Humeral Shaft Fractures
Bibliographic record
Abstract
BACKGROUND: This study aims to determine the incidence of pre- and postoperative nerve injuries associated with humeral shaft fractures. METHODS: Three hundred eight humeral shaft fractures (Orthopaedic Trauma Association/Arbeitsgemeinschaft fur Osteosynthesefragen 12) underwent surgical treatment from 2009 to 2020 were reviewed. Nerve injury was identified by motor or sensory deficit. Patients were grouped by mechanism. Each cohort was evaluated for rate of nerve injury and exploration, onset of nerve recovery, and predictors of nerve injury. RESULTS: Twenty-four sustained gunshot wounds (GSWs), 73 high-energy injury mechanisms, and 211 low-energy injury mechanisms. Fifty-six preoperative and 14 postoperative nerve injuries were identified. Eight patients (33%) in the GSW cohort, 22(31%) with high-energy mechanisms, and 26 (13%) with low-energy mechanisms had a preoperative nerve injury (P < 0.001). One patient (4%) in the GSW cohort, 0 with high-energy mechanisms, and 13 (7%) with low-energy mechanisms had a postoperative nerve injury (P = 0.24). Preoperative nerve injuries from GSWs and high-energy mechanisms required more time for nerve recovery (6.8 vs. 5.2 vs. 4.0 months). Regression analysis showed that GSW (odds ratio = 4.79, P = 0.038, confidence interval = 1.79 to 15.87) and high-energy mechanisms (odds ratio = 2.34, P = 0.049, confidence interval = 1.004 to 5.784) were associated with preoperative nerve injury. CONCLUSIONS: GSWs and high-energy mechanisms have higher incidence of nerve injury associated with humeral shaft fractures and may require more time to recover.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".