P.229 Epidemiology and Outcomes of Neck Pain Following Surgery for Degenerative Cervical Radiculopathy
Bibliographic record
Abstract
Background: Many studies have demonstrated improved arm pain (AP) following surgery for degenerative cervical radiculopathy (DCR); however, axial neck pain (NP) is generally not felt to improve. The purpose of this study was to determine whether surgery for DCR improves NP. Methods: A ambispective cohort study of the Canadian Spine Outcomes Research Network (CSORN) registry for patients who received 1-level, 2-level, 3-level ADCF (anterior cervical discectomy and fusion) or cervical disc arthroplasty (CDA) for DCR. Outcomes: 12-month post-operative Visual Analogue Scale for NP (VAS-NP), Neck Disability Index (NDI), VAS for AP (VAS-AP), Short-Form Physical Health Composite Scale (SF36-PCS), and Mental Health Composite Scale (SF36-MCS). Results: We identified 603 patients with DCR. CDA patients were the youngest (ANOVA; p<0.001). Patients reported similar pre-operative AP, NP, disability, and health-related quality of life, regardless of procedure (ANOVA; all P>0.05). All procedures offered a statistically significant reduction in VAS-NP, VAS-AP, and NDI (ANOVA; all P<0.001). Mean change from baseline in NP, AP, and disability, were similar across procedures. At 12 months, mean reduction in VAS-AP, VAS-NP, and NDI exceeded minimal clinically important differences for nearly all procedures. Conclusions: Patients undergoing surgery for DCR can expect a clinically significant, approximate 50% reduction in NP, AP, and neck-related disability.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".