Identification of Optimal Candidates for Operative Management of Mild Degenerative Cervical Myelopathy
Bibliographic record
Abstract
STUDY DESIGN: Retrospective cohort study using prospectively accrued data. OBJECTIVE: To describe the functional recovery trajectories after surgery for mild degenerative cervical myelopathy (DCM) and identify trajectory-associated preoperative factors. SUMMARY OF BACKGROUND DATA: Indications for surgical intervention for mild DCM remain a topic of discussion and uncertainty. We sought to address the hypothesis that optimal surgical candidates with mild DCM could be identified based on their predicted postoperative functional recovery after surgery. MATERIALS AND METHODS: We identified patients who underwent surgical decompression for mild DCM (modified Japanese Orthopedic Association score 15-17) enrolled in the prospective, multicenter AO Spine CSM-NA and CSM-I trials. Patients were classified using trajectory modelling into distinct recovery trajectories for their mJOA and Short Form 36, version 2 Physical Component Summary (SF36-PCS) scores over a 2-year follow-up. Predictors of recovery trajectories were identified using multivariate logistic regression. RESULTS: Of 198 patients with mild DCM, two distinct functional recovery trajectories for mJOA and two trajectories for SF36-PCS were identified. The good recovery trajectory for mJOA included 138 patients (69.7%) that achieved clinically important improvements in their function through 2-year follow up while 60 patients (30.3%) followed a marginal recovery trajectory, whereas the SF36-PCS good recovery trajectory group captured 166 patients (59.5%), and 79 patients (40.5%) in the marginal recovery group. Achieving good recovery in both mJOA and SF36-PCS was associated with higher self-reported baseline physical functioning. Patients who were older or current or former tobacco smokers were less likely to have a good postoperative recovery. CONCLUSION: Most mild DCM patients achieve clinically important recoveries of their function and self-reported physical function after surgery. However, a heterogeneous group of patients does not improve after surgical management. Further prospective studies are needed to evaluate clinically relevant factors associated with varying postoperative trajectories. LEVEL OF EVIDENCE: Level 3.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".