Impact of weight trajectory after bariatric surgery on co-morbidity evolution and burden
Bibliographic record
Abstract
Abstract Background Bariatric surgery in its various forms has been shown to be an effective intervention for weight management in select patients. Different types of surgery such as Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy demonstrate different efficacies in weight loss and co-morbidity resolution. Even within a single type of surgery, different patients respond differently and have differing weight-change trajectories post-surgery. The present analysis explores how improving a patient’s post-surgical weight change could impact on co-morbidity prevalence, treatment and associated costs in the Canadian setting. Methods Published data were used to derive statistical models to predict weight loss and co-morbidity evolution after RYGB. A 100-patient cohort was compared for an optimal versus a poor weight trajectory over a 10-year time horizon after surgery. Costs (2018 CAD$) were considered from the Canadian public payer perspective for diabetes, hypertension and dyslipidaemia. Robustness of results was assessed using probabilistic sensitivity analyses using the R language. Results Models fitted to patient data for total weight loss and co-morbidity evolution (resolution and new onset) demonstrated good fitting. Having a good versus poor weight trajectory resulted in a decreased burden, saving 181, 817, and 530 patient-years of diabetes, hypertension and dyslipidaemia treatment respectively. Cohorts on a good weight trajectory following RYGB had $1.9 million lower costs at 10 years than those on a poor weight trajectory. Conclusions Within a cohort of patients receiving the same type of bariatric surgery, achieving a good versus a poor weight loss trajectory can have a significant impact on outcomes by reducing the number of patient years of co-morbidity treatment and corresponding cost. Given the burden associated with a poor weight trajectory, health care systems should consider how best to ensure that more patients achieve a good long-term weight trajectory after 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".