Epidemiology of peripheral nerve and brachial plexus injuries in a trauma population
Bibliographic record
Abstract
BACKGROUND: Understanding patterns of peripheral nerve injuries (PNIs) and brachial plexus injuries (BPIs) is essential to preventing and appropriately managing nerve injuries. We sought to assess the incidence, cause, and severity of PNIs and BPIs sustained by patients with trauma. METHODS: We conducted a retrospective review of the Trauma Registry Database (January 2002 to December 2020) to identify patients with PNIs or BPIs. RESULTS: We evaluated data from 24 905 patients with trauma; 335 (1.3%) sustained PNIs (81% male; mean age 36 yr, standard deviation [SD] 16 yr) and 64 (0.3%) sustained BPIs (84% male; mean age 35, SD 15 yr). Nerves in the upper extremities were more commonly affected than those in the lower extremities. Sharp injuries (39.4%) and motorcycle accidents (32.8%) were the most frequent causes of PNIs and BPIs, respectively. Other common causes of PNI were motor vehicle collisions (16.7%) and gunshot wounds (12.8%). Many patients with PNIs (69.0%) and BPIs (53%) underwent operative management. The most frequent reconstruction for PNI was primary nerve repair (66%), while nerve transfers (48%) were more frequently used for BPI. CONCLUSION: Nerve injuries in the trauma population have decreased over the last 3 decades with shifts in mechanisms of injury and use of imaging, electrodiagnostic tests, and surgery. Nerve injuries are often complex and time-sensitive to treat; understanding changes in trends is important to ensure optimal patient management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".