Predictive factors for diabetes remission after bariatric surgery
Bibliographic record
Abstract
Background: Bariatric surgery has been shown to induce type 2 diabetes mellitus (T2DM) remission in severely obese patients. After laparoscopic Roux-en-Y gastric bypass (LRYGB), diabetes remission occurs early and independently of weight loss. Previous research has identified preoperative factors for remission, such as duration of diabetes and HbA1c. Understanding factors that predict diabetes remission can help to select patients who will benefit most from bariatric surgery. Methods: We retrospectively reviewed all T2DM patients who underwent laparoscopic sleeve gastrectomy (LSG) or LRYGB between January 2008 and July 2014. The primary outcome was diabetes remission, defined as the absence of hypoglycemic medications, fasting blood glucose < 7.0 mmol/L and HbA1c < 6.5%. Data were analyzed using multivariable logistic regression analysis to identify predictive factors of diabetes remission. Results: We included 207 patients in this analysis; 84 (40.6%) had LSG and 123 (59.4%) had LRYGB. Half of the patients (49.8%) achieved diabetes remission at 1 year. Multivariable logistic analysis showed that LRYGB had higher odds of diabetes remission than LSG (odds ratio [OR] 6.58, 95% confidence interval [CI] 2.79–15.50, p < 0.001). Shorter duration of diabetes (OR 0.91, 95% CI 0.83–0.99, p = 0.032) and the absence of long-acting insulin (OR 0.0011, 95% CI < 0.000–0.236, p = 0.013) predicted remission. Conclusion: Type of bariatric procedure (LRYGB v. LSG), shorter duration of diabetes and the absence of long-acting insulin were independent predictors of diabetes remission after bariatric surgery.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 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".