Bariatric-Metabolic Surgery Utilisation in Patients With and Without Diabetes: Data from the IFSO Global Registry 2015–2018
Bibliographic record
Abstract
Abstract Background Comparative international practice of patients undergoing bariatric-metabolic surgery for type 2 diabetes mellitus (T2DM) is unknown. We aimed to ascertain baseline age, sex, body mass index (BMI) and types of operations performed for patients with T2DM submitted to the IFSO Global Registry. Materials and Methods Cross-sectional analysis of patients having primary surgery in 2015–2018 for countries with ≥90% T2DM data completion and ≥ 1000 submitted records. Results Fifteen countries including 11 national registries met the inclusion criteria. The rate of T2DM was 24.2% (99,537 of 411,581 patients, country range 12.0–55.1%) and 77.1% of all patients were women. In every country, patients with T2DM were older than those without T2DM (overall mean age 49.2 [SD 11.4] years vs 41.8 [11.9] years, all p < 0.001). Men were more likely to have T2DM than women, odds ratio (OR) 1.68 (95% CI 1.65–1.71), p < 0.001. Men showed higher rates of T2DM for BMI <35 kg/m2 compared to BMI ≥35.0 kg/m2, OR 2.76 (2.52–3.03), p < 0.001. This was not seen in women, OR 0.78 (0.73–0.83), p < 0.001. Sleeve gastrectomy was the commonest operation overall, but less frequent for patients with T2DM, patients with T2DM 54.9% vs without T2DM 65.8%, OR 0.63 (0.63–0.64), p < 0.001. Twelve out of 15 countries had higher proportions of gastric bypass compared to non-bypass operations for T2DM, OR 1.70 (1.67–1.72), p < 0.001. Conclusion Patients with T2DM had different characteristics to those without T2DM. Older men were more likely to have T2DM, with higher rates of BMI <35 kg/m2 and increased likelihood of food rerouting operations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".