Analysis of Patient Attrition in a Publicly Funded Bariatric Surgery Program
Bibliographic record
Abstract
BACKGROUND: Obesity is a global epidemic, and several surgical programs have been created to combat this public health issue. Although demand for bariatric surgery has grown, so too has the attrition rate. In this study we identify patient characteristics and operational interventions that have contributed to high attrition in a multistage, multidisciplinary bariatric surgery program. STUDY DESIGN: A retrospective study was conducted of 1,682 patients referred for bariatric surgery at the University Health Network in Toronto, Canada, from June 2008 to July 2011. Demographic information, presurgical assessment dates, and records describing operational changes were collected. Several penalized likelihood and mixed effects multivariable logistic regression models were used to determine whether patient characteristics, operational changes, and previous experience affected program completion and intermediate transitions between assessments. RESULTS: Although the majority of attrition appears to be the result of patient self-removal, males (odds ratio [OR] 0.511, 95% CI 0.392 to 0.663, p < 0.001), and individuals with active substance use (OR 0.223, 95% CI 0.096 to 0.471, p < 0.001) were less likely to undergo surgery. Operational practices had a detrimental effect on program completion (OR 0.590, 95% CI 0.456 to 0.762, p < 0.001). Conversely, patients with a BMI > 40 kg/m(2) (OR 1.756, 95% CI 1.233 to 2.515, p = 0.002) and those who lived within 25 to 300 km of the center (OR > 1.633, p < 0.001) were more likely to undergo surgery. CONCLUSIONS: Certain subgroups in the referral population were found to be at a higher risk of noncompletion. Specialized care pathways must be implemented to address this issue. Furthermore, careful consideration must be given to operational decisions because they may negatively affect access to care, as we have shown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".