Effect of Roux-en-Y gastric bypass on pharmacologic dependence in obese patients with type 2 diabetes
Bibliographic record
Abstract
Background: More than half the diabetes-related health care costs in Canada relate to drug costs. We aimed to determine the effect of Roux-en-Y gastric bypass (RYGB) on the use of insulin and orally administered hypoglycemic medications in patients with diabetes. We also looked to determine overall cost savings with the procedure. Methods: We reviewed the bariatric clinic records of all patients with a confirmed diagnosis of type 2 diabetes mellitus who underwent RYGB between 2010/11 and 2014/15. Percentage estimated weight loss was recorded at 1 year, along with reductions in glycated hemoglobin (HbA1c) level and use of oral hypoglycemic therapy and insulin. We estimated medication costs using Manitoba-specific pricing data. Results: Fifty-two patients with at least 12 months of complete follow-up data were identified. The mean percentage estimated weight loss was 50.2%. The mean HbA1c level decreased from 7.6% to 6.0%, the mean number of orally administered hypoglycemics declined from 1.6 to 0.2, and the number of patients receiving insulin decreased from 18 (35%) to 3 (6%) (all p < 0.001). The rate of resolution of type 2 diabetes was 71%. Estimated mean annual per-patient medication costs decreased from $508.56 to $79.17 (p < 0.001). Potential overall health care savings could total $3769 per patient in the first year, decreasing to $1734 at 10 years. Conclusion: Roux-en-Y gastric bypass resulted in significant improvement in diabetic control, with a reduction in hypoglycemic medication use and associated costs in the early postoperative period. Potentially, large indirect and direct cost savings can be realized in the longer term.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".