Chronic pain after decompression for degenerative cervical myelopathy: a pooled trajectory analysis of individual participant data
Bibliographic record
Abstract
ABSTRACT: Pain is a significant contributor to quality of life for those living with degenerative cervical myelopathy (DCM). The trajectories and factors associated with chronic pain are poorly understood. Patients with DCM were identified from a harmonized data set of the AO Spine Cervical Spondylotic Myelopathy (CSM)-North America, CSM-International, and CSM-Protect studies. Pain scores were prospectively collected using the Neck Disability Index pain intensity (NDI-PI) score preoperatively and at 6-month, 12-month, and 24-month follow-up. Patients were categorized into 3 groups of preoperative pain: severe pain (NDI-PI ≥3), moderate pain (NDI-PI = 2), and minimal pain (NDI-PI ≤1). Latent class trajectory modeling classified patients into distinct trajectories based on their NDI-PI score over 24 months postoperatively. From a total of 952 patients, 32% of patients (n = 305) presented preoperatively with severe pain, 29.1% (n = 277) with moderate pain, and 38.9% (n = 370) with minimal pain. Postoperatively, patients presenting with severe pain followed (1) complete resolution (n = 128, 42.0%), (2) moderate recovery (n = 105, 34.4%), or (3) marginal recovery (n = 72, 23.6%) trajectory. Patients presenting with moderate pain followed the trajectories of (1) pain evolution (n = 22, 7.9%), (2) marginal recovery (n = 104, 37.6%), and (3) complete resolution (n = 151, 54.5%). Patients presenting with minimal pain followed 2 trajectories: (1) no pain evolution (n = 329, 88.9%) and (2) moderate evolution (n = 41, 11.1%). At 24 months, 36.1% (n = 344) of all trajectories ended in chronic pain. Preoperative pain in DCM can be classified into distinct subpopulations with fundamentally differing clinical courses. Surgery is associated with long-term trajectories of pain reduction in painful DCM. However, some patients experience persisting chronic pain.
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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 | 0.033 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".