Regional Variations in the Public Delivery of Bariatric Surgery
Bibliographic record
Abstract
OBJECTIVE: We evaluated regional access to bariatric surgery within the high-volume, center of excellence (COE) model of Ontario, Canada. BACKGROUND: In 2009, Ontario implemented Canada's first regionalized bariatric surgical care system based on a COE. Because of this, a small number of COEs service a large population and geographic area. METHODS: This study identified all patients older than 18 years, who received bariatric surgery from April 2009 to March 2012. Morbid obesity-adjusted rates of surgery were then calculated for each neighborhood, and a cluster analysis was performed to determine aggregation of neighborhoods with significantly higher (hot spots) or lower (cold spots) rates of surgery. Ordinal logistic regression was used to identify independent predictors of neighborhood access. RESULTS: The cluster analysis identified 49 cold spot neighborhoods, representing 1.7 million people. Forty of these neighborhoods lie within a relatively small area that contains 3 of the 4 COEs. In the multivariate analysis, for every 100 km from the nearest COE, neighborhoods were 0.88 times as likely to live in a hot spot [95% CI (confidence interval): 0.80-0.97; P = 0.012]. In addition, having a bariatric facility within the same administrative health region as the neighborhood made it almost twice as likely to be a hot spot, odds ratio = 1.75 (95% CI: 1.10-2.79; P = 0.018). Low neighborhood socioeconomic status was not associated with decreased delivery of care. CONCLUSIONS: This study identified an unequal delivery of bariatric surgery within Ontario. Both longer distances and not having a bariatric facility within the same health region had significant negative effects. Further research into patient attitudes and referral patterns is required to better characterize these disparities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".