A Clinical Prediction Rule for Clinical Outcomes in Patients Undergoing Surgery for Degenerative Cervical Myelopathy: Analysis of an International AOSpine Prospective Multicenter Data Set of 743 Subjects
Bibliographic record
Abstract
Introduction Knowledge of important clinical predictors of surgical outcome can provide decision support to surgeons and enable them to appropriately manage their patients' expectations. This study aims to determine the most important global clinical predictors of surgical outcome in patients undergoing surgery for CSM, based on data from two multicenter prospective studies. Patients and Methods A total of 743 surgical patients with CSM participated in either the CSM-North America or CSM-International study. The model was developed to distinguish between patients with mild myelopathy postoperatively (mJOA ≥ 16) and those with substantial residual neurological impairment (mJOA < 16). Univariate analyses were performed to evaluate the relationship between outcome and various clinical predictors. Multivariate logistic regression was used to formulate the final prediction model. Results Univariate analyses demonstrated that the odds of achieving a score ≥ 16 decreased with the presence of certain symptoms, including impaired gait; the presence of certain signs such as lower limb spasticity; positive smoking status; a higher comorbidity score; more severe preoperative myelopathy; and older age. The final prediction model included age (OR = 0.97, p = 0.0017), duration of symptoms (OR = 0.88, p = 0.049), smoking status (OR = 0.51, p = 0.0018), impairment of gait (OR = 1.94, p = 0.0168), broad-based unstable gait (OR = 1.75, p = 0.0133), baseline severity (OR = 1.23, p < 0.0001), and comorbidity score (OR = 0.84, p = 0.0030). Conclusion Patients are more likely to achieve a score ≥ 16 if they are younger, have a shorter duration of symptoms, are less severe preoperatively, do not smoke, and do not have comorbidities or evidence of gait dysfunction.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".