P.205 Pain has a significant impact on post-operative quality of life outcomes in patients requiring surgical intervention for degenerative cervical myelopathy
Bibliographic record
Abstract
Background: Degenerative Cervical Myelopathy (DCM) is a progressive condition causing cervical spinal cord injury. Disease severity is commonly assessed using the modified Japanese Orthopedic Association (mJOA) score, yet clinical guidelines do not integrate pain—a key symptom—in evaluations. This meta-analysis examines the relationship between pain scores and quality of life outcomes (QOL) in surgical DCM patients. Methods: A comprehensive literature search using MEDLINE, Web of Science, and Embase identified 73 studies. Data regarding pain scores (VAS/NRS) and QOL outcomes (SF-12, SF-36) were extracted by 2 independent reviewers and all conflicts were resolved by the senior author. The number of patients analyzed in the studies included was 929. Results: Meta-regression identified no significant relationship between pain and SF-36 preoperatively but found a significant negative correlation at 3 months (r = -0.67, p<0.05), 6 months (r = -0.65, p<0.05), 1 year (-0.63, p<0.05), and 2 years (r = -0.62, p<0.05). Conclusions: Our results indicate a strong relationship between postoperative pain and QOL among patients with DCM. Surgeons and care teams should prioritize optimal pain management postoperatively for patients with DCM.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.016 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".