Management of brachial plexus injuries in adults: Clinical evaluation and diagnosis
Bibliographic record
Abstract
Brachial plexus injuries are devastating injuries that usually affect the younger population. The usual modes of injuries are roadside accidents, falls, and assaults. The affected individuals are crippled and may suffer from excruciating peripheral or central deafferentation pain for rest of their lives. The loss of functional capacity accounts for a significant number of man-hours lost at the workplace and consequent financial burden on the family. The results of brachial plexus reconstructive surgery have generally been unsatisfactory in the past. However, in recent decades, the efficacy of surgery has been proven beyond doubt, and there have been various published series in literature that have reported a good outcome after surgical management of these injuries. This has been made possible by the use of operating microscopes, better microsuture techniques for nerve graft and nerve or tendon transfer repair, and advanced perioperative electrophysiological techniques. The key to successful management lies in the proper clinical evaluation, supplemented with electrophysiology, preoperative imaging studies, and planning of surgical strategy. The partial injuries have a better outcome as compared with global palsies, and early referral should be emphasized. Selective combinations of nerve graft and transfers provide a moderate shoulder and elbow control. However, a multispecialty approach involving hand surgeons, plastic surgeons, and physiotherapists is required.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".