Patient-Reported Outcomes Following Surgery for Lumbar Disc Herniation: Comparison of a Universal and Multitier Health Care System
Bibliographic record
Abstract
Study Design Ambispective cohort study. Objective Canada has a government-funded universal health care system. The United States utilizes a multitier public and private system. The objective is to investigate differences in clinical outcomes between those surgically treated for lumbar disc herniation in a universal health care and multitier health system. Methods Surgical lumbar disc herniation patients enrolled in the Canadian Spine Outcome Research Network (CSORN) were compared with the surgical cohort enrolled in the Spine Patients Outcome Research Trial (SPORT) study. Baseline demographics and spine-related patient-reported outcomes (PROs) were compared at 3 months and 1 year post-operatively. Results The CSORN cohort consisted of 443 patients; the SPORT cohort had 763 patients. Patients in the CSORN cohort were older (46.4 ± 13.5 vs 41.0 ± 10.8, P < .001) and were more likely to be employed (69.5% vs 60.3%, P = .003). The CSORN cohort demonstrated significantly greater rates of satisfaction after surgery at 3 months (87.2% vs 64.8%, P < .0001) and 1 year (85.6% vs 69.6%, P < .0001). Improvements in back and leg pain followed similar trajectories in the two cohorts, but there was less improvement on ODI in the CSORN cohort ( P < .01). On multivariable logistic regression, the CSORN cohort was a significant independent predictor of patient satisfaction at 1-year follow-up ( P < .001). Conclusions Despite less improvement on ODI, patients enrolled in CSORN, as part of a universal health care system, reported higher rates of satisfaction at 3 months and 1 year post-operatively compared to patients enrolled within a multitier health system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".