Comorbidities as an Indication for Metabolic Surgery
Bibliographic record
Abstract
Metabolic diseases, comprising type 2 diabetes mellitus (T2DM), dyslipidemia, and non-alcoholic steatohepatitis (NASH), are rapidly increasing worldwide. Conservative medical therapy, including the newly available drugs, has only limited effects and does neither influence survival or the development of micro- or macrovascular complications, nor the progression of NASH to liver cirrhosis, nor the development of hepatocellular carcinomas in the NASH liver. In contrast, metabolic surgery is very effective independent of the preoperative body mass index (BMI) in reducing overall and cardiovascular mortality in patients with T2DM. Furthermore, metabolic surgery significantly reduces the development of micro- and macrovascular complications while being the most effective therapy in order to achieve remission of T2DM and to reach the targeted glycemic control. Importantly, even existing diabetic complications such as nephropathy as well as the features of NASH can be reversed by metabolic surgery. Here, we propose indications for metabolic surgery due to T2DM and NASH based on a simple but objective, disease-specific staging system. We outline the use of the Edmonton Obesity Staging System (EOSS) as a clinical staging system independent of the BMI that will identify patients who will benefit the most from metabolic surgery.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".