Clinical and Surgical Predictors of Specific Complications following Surgery for the Treatment of Degenerative Cervical Myelopathy: Results from the Multicenter, Prospective AOSpine International Study on 479 Patients
Bibliographic record
Abstract
Introduction This study aimed to identify important clinical and surgical predictors of perioperative complications in patients with CSM. This knowledge will help clinicians recognize their high-risk patients and allow them to institute appropriate prevention plans. Material and Methods This study aimed to identify important clinical and surgical predictors of perioperative complications in patients with CSM. This knowledge will help clinicians recognize their high-risk patients and allow them to institute appropriate prevention plans. Results A total of 80 patients experienced 92 perioperative complications (16.7%). Univariately, the major clinical risk factors were OPLL ( p = 0.022), the number of comorbidities ( p = 0.020), diabetes ( p = 0.004), and coexisting gastrointestinal disorders ( p = 0.045). Patients undergoing a two-stage surgery and those with a longer operative duration were also at a greater risk of perioperative complications. A final model consisted of diabetes (OR = 2.35, p = 0.039), age (OR = 1.02, 0.25), operative duration (OR = 1.003, p = 0.17), two-stage surgery (OR = 20.37, p = 0.012), OPLL (OR = 1.82, p = 0.064), gastrointestinal comorbidities (OR = 2.53, p = 0.020), and BMI (OR = 1.06, p = 0.10). Conclusion Patients are at a higher risk of perioperative complications if they are older; have OPLL, a higher BMI, diabetes or gastrointestinal disorders; and if they undergo a two-stage surgery and a long operation.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".