THE IMPACT OF SURGERY ON PAIN IN DEGENERATIVE CERVICAL MYELOPATHY: A POOLED ANALYSIS OF 1,047 PATIENTS FROM CSM-NA, CSM-I, AND CSM-PROTECT TRIALS
Bibliographic record
Abstract
Pain is a significant contributor to quality of life in degenerative cervical myelopathy (DCM). However, its trajectory and factors associated with prolonged chronic pain and resolution is poorly understood. Our study aimed to investigate the impact of surgery on pain in DCM over 12 months. DCM patients with severe pain were queried using a harmonized dataset of the AOSpine CSM-North America, CSM-International, and CSM-Protect clinical trials. Severe acute pain was characterized as a Neck Disability Index pain intensity (NDI-PI) score of 3 or greater. Latent class trajectory modelling (LCTM) was applied to classify patients into distinct trajectories based on their NDI-PI score over the initial 12 months postinjury. Predictors of recovery trajectories were identified using descriptive statistics and multinomial logistic regression with relative risk ratios (RRR) on demographic and surgical variables. From a total of 1,047 patients, three distinct recovery trajectories were discovered from our analysis of 305 patients with severe baseline pain (29.1%). Their parabolic course was classified as: 1) complete resolution of pain (n=134, 43.9%), 2) Recovery to moderate pain (n=105, 34.4%), and 3) marginal recovery (n=72, 23.6%). In patients with severe baseline pain, older age, being married, and higher baseline Nurick and mJOA scores were associated with complete resolution. Anxiety and depression were inversely associated with complete resolution. Surgically, complete pain resolution was associated with anterior autograft with autograft, cage, and allograft. Posterior autograft was inversely associated with complete resolution. Severe acute pain can be classified into one of three distinct subpopulations with fundamentally differing clinical courses. There is greater than 50% of unresolved chronic in DCM patients presenting with severe acute pain over the course of 12 months. Given the high prevalence of chronic pain and impact to quality of life, factors associated with pain trajectories may be avenues for futures comparative studies.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.013 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".