Impact of weight trajectory after bariatric surgery on co-morbidity evolution and burden
Bibliographic record
Abstract
BACKGROUND: Bariatric surgery, such as Roux-en-Y gastric bypass [RYGB] has been shown to be an effective intervention for weight management in select patients. After surgery, different patients respond differently even to the same surgery and have differing weight-change trajectories. The present analysis explores how improving a patient's post-surgical weight change could impact 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. Burden in the form of patient-years of co-morbidity treatment and associated costs was estimated for a 100-patient cohort on one of 6 weight trajectories, and for real-world simulations of mixed patient cohorts where patients experience multiple weight loss outcomes over a 10-year time horizon after RYGB surgery. Costs (2018 Canadian dollars) 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. Improvement of 100 patients from the worst to the best weight loss trajectory was associated with a 50% reduction in 10-year co-morbidity treatment costs, decreasing to a 27% reduction for an intermediate improvement. Results applied to mixed trajectory cohorts revealed that broad improvements by one trajectory group for all patients were associated with 602, 1710 and 966 patient-years of treatment of type 2 diabetes, hypertension and dyslipidaemia respectively in Ontario, the province of highest RYGB volume, corresponding to a cost difference of $3.9 million. CONCLUSIONS: Post-surgical weight trajectory, even for patients receiving the same surgery, can have a considerable impact on subsequent co-morbidity burden. Given the potential for alleviated burden associated with improving patient trajectory after RYGB, health care systems may wish to consider investments based on local needs and available resources to ensure that more patients achieve a good long-term weight trajectory.
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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 | 0.015 |
| 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 | 0.001 |
| 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".