Brachial Plexopathy After Cervical Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Retrospective, multicenter case-series study and literature review. OBJECTIVES: To determine the prevalence of brachial plexopathy after cervical spine surgery and to review the literature to better understand the etiology and risk factors of brachial plexopathy after cervical spine surgery. METHODS: A retrospective case-series study of 12 903 patients at 21 different sites was performed to analyze the prevalence of several different complications, including brachial plexopathy. A literature review of the US National Library of Medicine and the National Institutes of Health (PubMed) database was conducted to identify articles pertaining to brachial plexopathy following cervical spine surgery. RESULTS: In our total population of 12 903 patients, only 1 suffered from postoperative brachial plexopathy. The overall prevalence rate was thus 0.01%, but the prevalence rate at the site where this complication occurred was 0.07%. Previously reported risk factors for postoperative brachial plexopathy include age, anterior surgical procedures, and a diagnosis of ossification of the posterior longitudinal ligament. The condition can also be due to patient positioning during surgery, which can generally be detected via the use of intraoperative neuromonitoring. CONCLUSIONS: Brachial plexopathy following cervical spine surgery is rare and merits further study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".