Disparities in Receipt of Bariatric Surgery in Canada
Bibliographic record
Abstract
BACKGROUND: Patients with lower socioeconomic status (SES) in the United States have reduced access to many health services including bariatric surgery. It is unclear whether disparities in bariatric surgery exist in countries with government-sponsored universal health benefits. The authors used data from a large regional Canadian bariatric surgery referral center to examine the relationship between SES and receipt of bariatric surgery. METHODS: The Toronto Western Hospital bariatric surgery registry was used to identify all adults referred for bariatric surgery assessment from 2010 to 2017. The authors compared demographics, SES measures, and clinical measures among patients who did not and did undergo bariatric surgery (Roux-en-Y or sleeve gastrectomy). Multiple logistic regression was used to examine differences in receipt of bariatric surgery according to patient demographic characteristics and SES factors. RESULTS: Among 2417 patients included in the study, 646 (26.7%) did not receive surgery and 1771 patients (73.2%) did. Patients who did not undergo surgery were more likely to be male individual (29.1% vs. 19.3%; P<0.001), black (12.1% vs. 8.3%; P=0.005), South Asian/Middle Eastern (8.2% vs. 4.5%; P<0.001), and less likely to be white (68.9% vs. 76.7%; P<0.001). In multiple logistic regression, factors associated with not receiving surgery were male sex, Black and South Asian/Middle Eastern ethnicity, being single, lack of employment, and history of psychiatric illness. CONCLUSIONS: Among patients referred for bariatric surgery, those who were male individuals, nonwhite, single, and unemployed were less likely to undergo surgery. Our results suggest that even with equal insurance, there are disparities in receipt of bariatric 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.004 | 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".