Treatment and Outcomes of Pediatric Traumatic Brachial Plexus Injuries
Bibliographic record
Abstract
Introduction: Traumatic brachial plexus injuries (TBPIs) in children are rare, and clinical characteristics and outcomes are poorly understood. This study provides a large descriptive cohort of pediatric TBPIs, including presentation, treatment, and outcomes, along with comparison analysis to brachial plexus birth injuries (BPBI). Methods: A retrospective cohort was created of patients aged ≤18 years with TBPI from a single institution between 2001 and 2021. Demographics, injury characteristics, and treatment details were collected. Outcome measures were summarized at presentation and latest follow-up, including Active Movement Scale (AMS), Toronto Test score, and Mallet classification. One hundred eighty-nine cases of BPBI from our institutional BPBI registry were used as a comparison. Results: = .30) scores. Conclusion: While TBPIs are rare in children, data from BPBI studies may help guide understanding of patterns and outcomes. Pediatric TBPIs have higher rates of global plexus injuries and primary nerve surgery. Functional scores for TBPI are worse at presentation but improve to levels comparable to BPBI after surgery, suggesting a ceiling effect after brachial plexus injuries, whether traumatic or birth-related. Key Concepts: (1)Traumatic brachial plexus injuries (TBPI) are rare in children, so an understanding of evaluation and treatment of the more common brachial plexus birth injuries (BPBI) can be helpful.(2)Pediatric TBPI patients have a higher rate of global injuries and need for primary nerve surgery than BPBI patients.(3)While functional scores for TBPI patients are worse at presentation than BPBI patients, they improve to similar levels with treatment. Level of Evidence: III.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".