Regional variation in lumbar spine surgery in Saskatchewan: a population-based analysis
Bibliographic record
Abstract
Background: Unexplained significant variation may suggest a quality care problem in a health care system. The objective of this study was to determine the extent of variance in spine surgery Saskatchewan and determine possible causes. Methods: Provincial billing records for new spine surgery consultations from May 2011 through October 2012 were correlated with subsequent lumbar surgery. Two tertiary centers (TC1 and TC2) were compared with reference to the Health Region of origin of the patient. Wait times for surgery and utilization of spine pathway clinics was analyzed. Results: TC1 had significantly higher rates of spine fusion and lumbar spine surgery. The percentage of new referrals that went to surgery was 14.0% in TC1 and 11.8% in TC2 (p<0.0001, Z-Test). Population-based calculation of the rate of new referrals was 1581/482387 = 0.33% for TC1 vs. 970/601739 = 0.16% for TC2 (p<0.0001, Z-Test). Utilization of the spine pathway clinic was lower and wait times for surgery were longer in TC1. Conclusions: Causes of regional variation are unknown and likely multifactorial. In Saskatchewan, the most striking variance was that the rate of primary care referrals for lower back conditions in regions served by TC1 was double that for TC2. This could potentially be reduced through more regionally consistent utilization of the spine pathway.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".