“Metabolic surgery in Asian patients with type 2 diabetes mellitus and body mass index less than 30kg/m2: A systematic review”
Bibliographic record
Abstract
Background: has not been widely reported. Methods: were considered. The quality of the studies was assessed using the Newcastle-Ottawa scale. Results: Of the 1175 studies screened, 21 studies (11 prospective and 10 retrospective), including 1005 patients, were selected. Only one study had a control group. The longest follow-up was 60 months. The results showed significant improvement in glycated hemoglobin (HbA1c), fasting blood glucose (FBG), 2-h plasma glucose (2hPG), homeostasis model assessment for insulin resistance index (HOMA-IR), fasting C-peptide, triglycerides, total cholesterol, and a reduction in the use of oral hypoglycemic agents/insulin at 12, 24, 36, and 60 months after metabolic surgery. The most common surgical complications observed were anemia (2.1 %-33 %), marginal ulcer (4.2 %-17.3 %), gastrointestinal bleeding (1.9 %-12 %), anastomotic leak (2.1 %-3.5 %), anastomotic stenosis (2.1 %-3.5 %), reoperation (1.18 %), and a mortality rate of zero. Conclusions: after metabolic surgery. Future research with controlled studies should focus on preoperative patient selection criteria beyond just the BMI cutoff.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".