C5 Palsy After Cervical Spine Surgery: A Multicenter Retrospective Review of 59 Cases
Bibliographic record
Abstract
Study Design: A multicenter, retrospective review of C5 palsy after cervical spine surgery. Objective: Postoperative C5 palsy is a known complication of cervical decompressive spinal surgery. The goal of this study was to review the incidence, patient characteristics, and outcome of C5 palsy in patients undergoing cervical spine surgery. Methods: We conducted a multicenter, retrospective review of 13 946 patients across 21 centers who received cervical spine surgery (levels C2 to C7) between January 1, 2005, and December 31, 2011, inclusive. P values were calculated using 2-sample t test for continuous variables and χ 2 tests or Fisher exact tests for categorical variables. Results: Of the 13 946 cases reviewed, 59 patients experienced a postoperative C5 palsy. The incidence rate across the 21 sites ranged from 0% to 2.5%. At most recent follow-up, 32 patients reported complete resolution of symptoms (54.2%), 15 had symptoms resolve with residual effects (25.4%), 10 patients did not recover (17.0%), and 2 were lost to follow-up (3.4%). Conclusion: C5 palsy occurred in all surgical approaches and across a variety of diagnoses. The majority of patients had full recovery or recovery with residual effects. This study represents the largest series of North American patients reviewed to date.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".