FUNCTIONAL OUTCOME OF BRACHIAL PLEXUS RECONSTRUCTION AFTER TRAUMA
Bibliographic record
Abstract
OBJECTIVE: Traumatic brachial plexopathies can be devastating injuries. In addition to motor and sensory deficits, pain and functional limitations can be equally debilitating. We sought to evaluate functional outcome and quality of life using statistically validated tools. METHODS: The authors identified a consecutive series of patients who underwent surgical repair of a brachial plexus injury by the same surgeon between 1997 and 2004 at the McGill University Health Center. Participating patients were sent a package containing the Short Form 36, the Disability of the Arm, Shoulder, and Hand questionnaire, a pain visual analog scale, and an additional question on their satisfaction with the surgery. Data was recorded and analyzed using statistical software (SPSS version 13.0 for Windows; SPSS, Inc., Chicago, IL). RESULTS: Thirty-one patients with a mean age of 32.7 years at the time of injury participated in this study. The mean time to surgery was 7.5 months, and the mean follow-up period was 42.7 months. Patients who underwent surgery within 6 months of injury scored consistently better on the Disability of the Arm, Shoulder, and Hand questionnaire (P = 0.03) and the Short Form 36 subscale scores. There was no difference between supra- and infraclavicular injuries; however, patients with root avulsion injuries were more likely to have pain (P = 0.04) and scored lower on the Disability of the Arm, Shoulder, and Hand questionnaire (P = 0.05). CONCLUSION: Statistically validated tools can be used to evaluate the quality of life, upper extremity function, and pain after brachial plexus repairs. Root avulsion injuries and delayed surgical repair correlated negatively with functional outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".