Validation of the Individualized Metabolic Surgery score in predicting long‐term remission of diabetes after duodenal switch‐type procedures
Bibliographic record
Abstract
AIM: To validate the Individualized Metabolic Surgery (IMS) score and assess long-term remission of type 2 diabetes (T2D) after duodenal switch (DS)-type procedures in patients with obesity. In addition, to help guide metabolic procedure selection for those patients categorized as having severe T2D. MATERIALS AND METHODS: This is a retrospective single cohort study of all patients with T2D and severe obesity, who underwent DS-type procedures at a single institution from December 2010 to December 2018. Study endpoints included validating the IMS score in our cohort and evaluating the impact of DS-type procedures on long-term (≥ 5 years) remission of T2D, especially in patients with severe disease. A receiver operator characteristic curve was used to assess the accuracy of the IMS score using the area under the curve (AUC). RESULTS: The study cohort included 30 patients with complete baseline and long-term glycaemic data after their index DS-type surgery. Twelve patients (40%) were classified with severe T2D, and the distribution of IMS-based severity groups was similar between our cohort and the original IMS study (P = .42). IMS scores predicted long-term T2D remission with AUC = 0.77. Patients with IMS-based severe diabetes achieved significantly higher long-term remission after DS-type procedures compared with gastric bypass and/or sleeve gastrectomy from the original IMS study (42% vs. 12%; P < .05). CONCLUSIONS: The IMS score properly classifies the severity of T2D in our study cohort and adequately predicts its long-term remission after DS-type procedures. While T2D remission decreases with more severe IMS scores, long-term remission remains high after DS-type procedures among patients with severe disease.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".