Predictors of outcome in patients with degenerative cervical spondylotic myelopathy undergoing surgical treatment: results of a systematic review
Bibliographic record
Abstract
PURPOSE: To conduct a systematic review of the literature to determine important clinical predictors of surgical outcome in patients with cervical spondylotic myelopathy (CSM). METHODS: A literature search was performed using MEDLINE, MEDLINE in Process, EMBASE and Cochrane Database of Systematic Reviews. Selected articles were evaluated using a 14-point modified SIGN scale and classified as either poor (<7), good (7-9) or excellent (10-14) quality of evidence. For each study, the association between various clinical factors and surgical outcome, evaluated by the (modified) Japanese Orthopaedic Association scale (mJOA/JOA), Nurick score or other measures, was defined. The results from the EXCELLENT studies were compared to the combined results from the EXCELLENT and GOOD studies which were compared to the results from all the studies. RESULTS: The initial search yielded 1,677 citations. Ninety-one of these articles, including three translated from Japanese, met the inclusion and exclusion criteria and were graded. Of these, 16 were excellent, 38 were good and 37 were poor quality. Based on the excellent studies alone, a longer duration of symptoms was associated with a poorer outcome evaluated on both the mJOA/JOA scale and Nurick score. A more severe baseline score was related with a worse outcome only on the mJOA/JOA scale. Based on the GOOD and EXCELLENT studies, duration of symptoms and baseline severity score were consistent predictors of mJOA/JOA, but not Nurick. Age was an insignificant predictor of outcome on any of the functional outcomes considered. CONCLUSION: The most important predictors of outcome were preoperative severity and duration of symptoms. This review also identified many other valuable predictors including signs, symptoms, comorbidities and smoking status.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".