The Effect of Perioperative Adverse Events on Long-Term Patient-Reported Outcomes After Lumbar Spine Surgery
Bibliographic record
Abstract
BACKGROUND: Perioperative adverse events (AEs) lead to patient disappointment and greater costs. There is a paucity of data on how AEs affect long-term outcomes. OBJECTIVE: To examine perioperative AEs and their impact on outcome after lumbar spine surgery. METHODS: A total of 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 mo postoperatively included the Owestry Disability Index (ODI), 12-Item Short-Form Health Survey (SF-12) Physical (PCS) and Mental (MCS) Component Summary scales, visual analog scale (VAS) leg and back, EuroQol-5D (EQ5D), and satisfaction. RESULTS: AEs occurred in 767 (21.6%) patients, and 85 (2.4%) patients suffered major AEs. Patients with major AEs had worse ODI scores and did not reach minimum clinically important differences at 2 yr (no AE: 25.7 ± 19.2, major: 36.4 ± 19.1, P < .001). Major AEs were associated with worse ODI scores on multivariable linear regression (P = .011). PCS scores were lower after major AEs (43.8 ± 9.5, vs 37.7 ± 20.3, P = .002). On VAS leg and back and EQ5D, the 2-yr outcomes were significantly different between the major and no AE groups (<0.01), but these differences were small (VAS leg: 3.4 ± 3.0 vs 4.0 ± 3.3; VAS back: 3.5 ± 2.7 vs 4.5 ± 2.6; EQ5D: 0.75 ± 0.2 vs 0.64 ± 0.2). SF12 MCS scores were not different. Rates of satisfaction were lower after major AEs (no AE: 84.6%, major: 72.3%, P < .05). CONCLUSION: Major AEs after lumbar spine surgery lead to worse functional outcomes and lower satisfaction. This highlights the need to implement strategies aimed at reducing AEs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".