P.219 The effect of peri-operative adverse events on long-term patient reported outcomes after lumbar spine surgery
Bibliographic record
Abstract
Background: Peri-operative adverse events (AE) lead to patient disappointment and greater costs. There is a paucity of data on how AEs affect long-term outcomes. The purpose of this study is to examine peri-operative AEs and their impact on outcome after lumbar spine surgery. Methods: 3556 consecutive patients undergoing surgery for lumbar degenerative disorders enrolled in the Canadian Spine Outcomes and Research Network were analyzed. AEs were defined using the validated Spine AdVerse Events Severity system. Outcomes at 3,12, and 24 months post-operatively included the Owestry Disability Index (ODI), SF-12 Physical (PCS) and Mental (MCS) scales, visual analog scale (VAS) leg and back, Euroqol-5D (EQ5D), and satisfaction. Results: Adverse events occurred in 767 (21.6%) patients, 85 (2.4%) suffered major AEs. Patients with major AEs had worse OD (physical disability) scores and did not reach minimum clinically important differences at 2 years (no AE 25.7±19.2, major: 36.4±19.1, p<0.001). Major AEs were associated with worse ODI (physical disability) scores on multivariable linear regression (p=0.011). Conclusions: Major AEs after lumbar spine surgery lead to worse functional outcomes and lower satisfaction. This highlights the need to implement strategies aimed at reducing adverse events.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".