Type 2 diabetes remission rates 1‐year post‐Roux‐en‐Y gastric bypass and validation of the <scp>DiaRem</scp> score: the Ontario Bariatric Network experience
Bibliographic record
Abstract
Roux-en-Y gastric bypass (RYGB) is associated with the remission of type 2 diabetes mellitus (DM). There are a number of scoring systems available that help predict type 2 diabetes remission rates after bariatric surgery; however, relatively few have been validated externally. The DiaRem score, comprised of four preoperative variables (age, haemoglobin A1c [HbA1c], sulfonylurea and insulin-sensitizing agent use and insulin use), allows for the identification of patients who are most likely to have DM remission following RYGB. Our primary objective was to determine the variables predictive of DM remission 1 year post-RYGB, determine how well the DiaRem score predicts DM remission 1 year post-RYGB and identify the optimal cut-off DiaRem score. The study is based on results of RYGB performed across multiple centres in Ontario, Canada, overseen by the Centre for Surgical Invention and Innovation in Hamilton, with direction from the Ontario Bariatric Network. Regression analysis was used to determine the predictive value of demographic and clinical variables and that of the DiaRem score. The optimal DiaRem cut-off score was determined using sensitivity and specificity analysis. Of 3874 patients in the Ontario Bariatric Registry between January 2010 and February 2015, 915 had complete 1-year follow-up data. Among these, 15 were not classified as having DM at baseline and were excluded. Of the remaining 900 patients with type 2 diabetes and who underwent RYGB surgery, 333 (37.0%) had DM remission at 1-year follow-up. Three of four DiaRem variables (age, HbA1c, insulin use), in addition to use of any hypoglycaemic agent, were associated with DM remission. DiaRem score had moderate predictive value. A DiaRem score cut-off of <5 had a sensitivity of 71.8% and specificity of 71.3%. This study provides guidance to clinicians in using the DiaRem score to inform the selection and prioritization of patients to ensure timely access to bariatric surgery for those who are likely to benefit the most.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".