The Effects of Peri-Operative Adverse Events on Clinical and Patient-Reported Outcomes After Surgery for Degenerative Cervical Myelopathy: An Observational Cohort Study from the Canadian Spine Outcomes and Research Network
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: There is a lack of data examining the effects of perioperative adverse events (AEs) on long-term outcomes for patients undergoing surgery for degenerative cervical myelopathy. We aimed to investigate associations between the occurrence of perioperative AEs and coprimary outcomes: (1) modified Japanese Orthopaedic Association (mJOA) score and (2) Neck Disability Index (NDI) score. METHODS: We analyzed data from 800 patients prospectively enrolled in the Canadian Spine Outcomes and Research Network multicenter observational study. The Spine AEs Severity system was used to collect intraoperative and postoperative AEs. Patients were assessed at up to 2 years after surgery using the NDI and the mJOA scale. We used a linear mixed-effect regression to assess the influence of AEs on longitudinal outcome measures as well as multivariable logistic regression to assess factors associated with meeting minimal clinically important difference (MCID) thresholds at 1 year. RESULTS: There were 167 (20.9%) patients with minor AEs and 36 (4.5%) patients with major AEs. The occurrence of major AEs was associated with an average increase in NDI of 6.8 points (95% CI: 1.1-12.4, P = .019) and reduction of 1.5 points for mJOA scores (95% CI: -2.3 to -0.8, P < .001) up to 2 years after surgery. Occurrence of major AEs reduced the odds of patients achieving MCID targets at 1 year after surgery for mJOA (odds ratio 0.23, 95% CI: 0.086-0.53, P = .001) and for NDI (odds ratio 0.34, 95% CI: 0.11-0.84, P = .032). CONCLUSION: Major AEs were associated with reduced functional gains and worse recovery trajectories for patients undergoing surgery for degenerative cervical myelopathy. Occurrence of major AEs reduced the probability of achieving mJOA and NDI MCID thresholds at 1 year. Both minor and major AEs significantly increased health resource utilization by reducing the proportion of discharges home and increasing length of stay.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".