Individualized Metabolic Surgery Score
Bibliographic record
Abstract
OBJECTIVE: To construct and validate a scoring system for evidence-based selection of bariatric and metabolic surgery procedures according to severity of type 2 diabetes (T2DM). BACKGROUND: Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) account for >95% of bariatric procedures in United States in patients with T2DM. To date, there is no validated model to guide procedure selection based on long-term glucose control in patients with T2DM. METHODS: A total of 659 patients with T2DM who underwent RYGB and SG at an academic center in the United States and had a minimum 5-year follow-up (2005-2011) were analyzed to generate the model. The validation dataset consisted of 241 patients from an academic center in Spain where similar criteria were applied. RESULTS: At median postoperative follow-up of 7 years (range 5-12), diabetes remission (HbA1C <6.5% off medications) was observed in 49% after RYGB and 28% after SG (P < 0.001). Four independent predictors of long-term remission including preoperative duration of T2DM (P < 0.0001), preoperative number of diabetes medications (P < 0.0001), insulin use (P = 0.002), and glycemic control (HbA1C < 7%) (P = 0.002) were used to develop the Individualized Metabolic Surgery (IMS) score using a nomogram. Patients were then categorized into 3 stages of diabetes severity. In mild T2DM (IMS score ≤25), both procedures significantly improved T2DM. In severe T2DM (IMS score >95), when clinical features suggest limited functional β-cell reserve, both procedures had similarly low efficacy for diabetes remission. There was an intermediate group, however, in which RYGB was significantly more effective than SG, likely related to its more pronounced neurohormonal effects. Findings were externally validated and procedure recommendations for each severity stage were provided. CONCLUSIONS: This is the largest reported cohort (n = 900) with long-term postoperative glycemic follow-up, which, for the first time, categorizes T2DM into 3 validated severity stages for evidence-based procedure selection.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".