P.218 Patient reported outcomes following surgery for lumbar disk herniation: comparison of a universal and multitier health care system.
Bibliographic record
Abstract
Background: Canada has a universal health care system that is funded by the government 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 lumbar disk herniation in Canada as compared to the United States. Methods: Surgical lumbar disk herniation patients enrolled in the Canadian Spine Outcome Research Network (CSORN) prospective 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. Results: The CSORN cohort consisted of 443 patients and the SPORT cohort was made up of 573 patients. Patients in the CSORN cohort were older (p<0.001), and were more likely to be employed (p=0.003). The CSORN cohort demonstrated significantly greater rates of satisfaction after surgery at 3 months (87.2% vs. 65.5%, p=0.003) and 1 year (85.6% vs. 69.0%, p<0.0001). The CSORN cohort was a significant independent predictor of patient satisfaction at 1 year. Conclusions: Patients undergoing surgical treatment for lumbar disc herniation in Canada reported higher rates of satisfaction at 3 months and 1 year post-operatively compared to the United States.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".