Defining and Using Preoperative Predictors of Diabetic Remission Following Bariatric Surgery
Bibliographic record
Abstract
BACKGROUND: Diabetes remission is defined as the return of glycemic control in the absence of medication or insulin use after bariatric surgery. We sought to identify and assess the clinical utility of a predictive model for remission of type 2 diabetes mellitus in a population seeking bariatric surgery. METHOD: A retrospective cohort design was applied to presurgical data on patients referred for Roux-en-Y gastric bypass (RYGB) or vertical sleeve gastrectomy (VSG). The model developed from logistic regression was compared with a published model through receiver operating characteristic analyses. RESULTS: At 12 months postoperatively, 59.7% of the cohort was remitted, with no differences between RYGB and VSG. Logistic regression analyses yielded a model in which 4 preoperative variables reliably predicted remission. A Hosmer-Lemeshow goodness-of-fit test result of 0.204 indicated good fit of the developed prediction model to our outcome data. The predictive accuracy of this prediction model was compared with a published model, and an associated variation with diabetes years was substituted for age in our patient population. Our model was the most accurate. CONCLUSIONS: Using these predictors, healthcare providers may be able to better counsel patients who are living with diabetes and considering bariatric surgery on the likelihood of achieving remission from the intervention. This refined prediction model requires further testing in a larger sample to evaluate its external validity.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".