Effectiveness of Surgical Decompression in Patients With Degenerative Cervical Myelopathy: Results of the Canadian Prospective Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Conflicting evidence exists regarding the effectiveness of surgery for degenerative cervical myelopathy (DCM), particularly in mild DCM. OBJECTIVE: To prospectively evaluate the impact of surgery on patient-reported outcomes in patients with mild (modified Japanese Orthopaedic Association [mJOA] ≥ 15), moderate (mJOA 12-14), and severe (mJOA < 12) DCM. METHODS: Prospective, multicenter cohort study of patients with DCM who underwent surgery between 2015 and 2019 and completed 1-yr follow-up. Outcome measures (mJOA, Neck Disability Index [NDI], EuroQol-5D [EQ-5D], Short Form [SF-12] Physical Component Score [PCS]/Mental Component Score [MCS], numeric rating scale [NRS] neck, and arm pain) were assessed at 3 and 12 mo postoperatively and compared to baseline, stratified by DCM severity. Changes in outcome measures that were statistically significant (P < .05) and met their respective minimum clinically important differences (MCIDs) were deemed clinically meaningful. Responder analysis was performed to compare the proportion of patients between DCM severity groups who met the MCID for each outcome measure. RESULTS: The cohort comprised 391 patients: 110 mild, 163 moderate, and 118 severe. At 12 mo after surgery, severe DCM patients experienced significant improvements in all outcome measures; moderate DCM patients improved in mJOA, NDI, EQ-5D, and PCS; mild DCM patients improved in EQ-5D and PCS. There was no significant difference between severity groups in the proportion of patients reaching MCID at 12 mo after surgery for any outcome measure, except NDI. CONCLUSION: At 12 mo after surgery, patients with mild, moderate, and severe DCM all demonstrated improved outcomes. Severe DCM patients experienced the greatest breadth of improvement, but the proportion of patients in each severity group achieving clinically meaningful changes did not differ significantly across most outcome measures.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| 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".