DESCRIBING AVAILABLE PREOPERATIVE EDUCATION METHODS AND COMPARING OUTCOMES FOR SPINAL FUSION CANDIDATES: A CROSS-CANADA STUDY
Bibliographic record
Abstract
This study aimed to (1) describe preoperative education practices among Canadian spine surgeons for patients undergoing spinal fusion and (2) investigate the effects of these recommendations on patient outcomes. We surveyed surgeons contributing to the Canadian Spine Outcomes and Research Network (CSORN) registry regarding their demographics, preoperative patient educational procedures, and perceptions of education. Retrospective data was incorporated from CSORN patients who underwent thoracolumbar fusion with 3- and 12-month follow-up. Survey responses established patient cohorts with or without access to a preoperative multidisciplinary education class. Study outcomes comprised patient satisfaction, expectation fulfillment, resource utilization, and length of stay (LOS). Propensity score models using inverse probability weights and regression adjustment accounted for confounding from age, sex, education, time with condition, principal pathology, baseline disability, and mental health. We included data from 31 surgeons (64.5% orthopaedic, 35.5% neurosurgeons) representing 16 centres and 448 patients (mean age 59.71 [± SD 11.86] years; 58.5% female; class group n = 130). Surgeons favoured using spine models (71.0%) and pamphlets (61.3%) among listed education tools. A patient preoperative education class was available to 6.5% of surgeons, while 12.9% selected none of the preoperative education opportunities. On a 10-point scale of preoperative education importance (0-not important, 10-very important), the mean for surgeon responses was 8.90 [± SD 1.30] (range 5-10) points. No differences were seen between education groups (with vs. without class access) for patient satisfaction and resource utilization at 3 or 12 months. The average LOS was 2.29 days shorter (p = 0.017) for the class group. The class group was more likely to achieve expectations for mental health (OR [95% CI] = 1.85 [1.12-3.06]) and return to recreational activities (2.07 [1.24-3.45]) at 12 months. Conversely, the class group showed decreased odds of meeting expectations for independence at 3 months (0.51 [0.31-0.83]). Recommending a multidisciplinary education class before surgery may confer benefits for patient outcomes, yet utilization in Canada is very limited. Addressing this gap nationally represents an opportunity to improve quality of care.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".