P.217 Patient reported outcomes following surgery for lumbar spinal stenosis: Comparison of a universal and multitier health care system
Bibliographic record
Abstract
Background: Canada has a universal health care system while the United States utilizes a combined public and private payer system. The purpose of this study is to investigate whether there are differences in clinical outcomes between those surgically treated for spinal stenosis in Canada as compared to the United States. Methods: Surgical lumbar spinal stenosis patients treated in Canada that were enrolled in the Canadian Spine Outcome Research Network (CSORN) prospective multicenter registry were compared with the surgical cohort enrolled in the Spine Patients Outcome Research Trial (SPORT) study. Spine-related patient reported outcomes (PROs) were compared at 3 months and 1 year post-operatively. Results: The CSORN cohort consisted of 432 patients and the SPORT cohort was made up of 278 patients. The CSORN cohort had a higher proportion of patients with a symptom duration greater than 6 months (92.3% vs. 58.3%, p<0.0001). The CSORN cohort demonstrated significantly greater rates of satisfaction after surgery at 3 months (p=0.003) and 1 year (p<0.001). Conclusions: Patients undergoing surgical treatment for lumbar spinal stenosis in Canada (CSORN cohort) reported higher rates of satisfaction at 3 months and 1 year post-operatively compared to the United States cohort (SPORT) despite having longer durations of symptoms prior to surgery.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".