Nerve Injury Severity and Outcomes
Bibliographic record
Abstract
Background: Nerve injuries (NI) after pediatric supracondylar humeral fractures (SHF) are commonly reported as neurapraxic, with most studies focusing on motor recovery alone. This study aimed to analyze both sensory and motor outcomes of NI in SHF to identify patterns of recovery and predictors of prolonged nerve recovery. Methods: A 4-year retrospective cohort study of children 0 to 12 years with NI after SHF was performed. Patterns of nerve recovery and outcomes of spontaneous recovery were analyzed descriptively. Predictors of prolonged nerve recovery (>4 months) were explored using multivariable logistic regression. Results: Of 1,137 children with SHF, 93 (52% male; mean age 7.2 ± 2.1 years) presented with traumatic NI. The median nerve was most frequently injured (62.4%, n = 58), followed by the radial (29.0%, n = 27) and ulnar nerves (8.6%, n = 8). All children had both sensory and motor deficits; there were no motor-only injuries. Complete spontaneous recovery occurred in 90 children, with 64% (n = 56) taking more than 4 months to recover. Two patients (2.2%) had neurotmetic injuries necessitating nerve grafting. Radial NI showed faster recovery, with 69.2% (n = 18) resolving within 4 months, compared with 20.6% (n = 12) and 25% (n = 2) of median and ulnar NI, respectively. Conclusions: Traumatic NI after pediatric SHF achieve full spontaneous recovery in most cases, but the majority take longer than 4 months to recover. Radial NI recover more rapidly (<4 months) than median or ulnar NI. Factors such as time to surgery, presence of a pink-pulseless limb, and multidirectional instability did not predict prolonged recovery in extension-type SHF. Level of Evidence: Level III. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".