Laparoscopic metabolic surgery for the treatment of type 2 diabetes in Asia: a scoping review and evidence-based analysis
Bibliographic record
Abstract
Laparoscopic metabolic surgery has been previously shown to be an effective treatment for obese patients with type 2 diabetes (T2DM). The objective of this scoping review is to determine the impact of metabolic surgery for the treatment of type 2 diabetes in Asia and perform an evidence-based analysis. We performed a literature search in PubMed for research on laparoscopic metabolic surgery for the treatment of T2DM in Asia region. We classified the included studies based on the Oxford Center for Evidence Based Medicine guidelines. And performed and evidence analysis. In total, 205 articles were identified. 62.9% of the studies were from East Asia. The evidence of 26 studies are level I, 59 are level II. Laparoscopic sleeve gastrectomy (LSG) was the most commonly reported surgical procedure (63.1%) in Asia. The number of laparoscopic metabolic surgery for T2DM in Asian countries has increased rapidly over the last 8 years. We identified 16 studies which showed that laparoscopic metabolic surgery is an effective and safe treatment for T2DM in patients with a BMI of > 25 kg/m 2 to < 35 kg/m 2 in Asia. Our results suggest that laparoscopic metabolic surgery might be an effective and safe treatment for T2DM patients with BMI < 35 kg/m 2 , and that LSG is the most commonly performed surgical procedure for this in Asia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".