Metabolic syndrome and its single traits as risk factors for diabetes in people with impaired glucose tolerance: the STOP-NIDDM trial
Bibliographic record
Abstract
The STOP-NIDDM trial was an international, double-blind, placebo-controlled randomised study in people with impaired glucose tolerance (IGT). They were treated with an alpha-glucosidase inhibitor, acarbose, to prevent diabetes; the overall number needed to treat (NNT) was 11. In a secondary analysis, we considered the impact of single traits and overall metabolic syndrome (MetS) respectively on risk of diabetes and NNT respectively. In all, there were 1,368 patients. They were followed up for 3.3 years, and the prevalence of MetS was 61%. Multivariate analysis revealed treatment group 2-hour (post-challenge) plasma glucose, glycosylated haemoglobin (HbA1C), triglycerides and leukocyte count as independent predictors. The annual incidence of diabetes in the placebo group with MetS was 18.7% vs. 11.2% in patients without MetS; the corresponding figures in the acarbose group were 13.5% and 9.4%, respectively. The NNT in patients was 5.8 in patients with MetS and 16.5 in those without MetS. In conclusion, most single traits and overall MetS label a very high-risk group in people with IGT. People with MetS reach a NNT to prevent development of new diabetes with acarbose of 5.8.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".