Time-saving screening for diabetes in patients with coronary artery disease: a report from EUROASPIRE IV
Bibliographic record
Abstract
BACKGROUND: WHO advocates 2-hour oral glucose tolerance test (OGTT) for detecting diabetes mellitus (DM). OGTT is the most sensitive method to detect DM in patients with coronary artery disease (CAD). Considered time consuming, the use of OGTT is unsatisfactory. A 1-hour plasma glucose (1hPG) test has not been evaluated as an alternative in patients with CAD. OBJECTIVES: To create an algorithm based on glycated haemoglobin (HbA1c), fasting plasma glucose (FPG) and 1hPG limiting the need of a 2-hour plasma glucose (2hPG) in patients with CAD. METHODS: 951 patients with CAD without DM underwent OGTT. A 2hPG≥11.1 mmol/L was the reference for undiagnosed DM. The yield of HbA1c, FPG and 1hPG was compared with that of 2hPG. RESULTS: Mean FPG was 6.2±0.9 mmol/L, and mean HbA1c 5.8±0.4%. Based on 2hPG≥11.1 mmol/L 122 patients (13%) had DM. There was no value for the combination of HbA1c and FPG to rule out or in DM (HbA1c≥6.5%; FPG≥7.0 mmol/L). In receiver operating characteristic analysis a 1hPG≥12 mmol/L balanced sensitivity and specificity for detecting DM (both=82%; positive and negative predictive values 40% and 97%). A combination of FPG<6.5 mmol/L and 1hPG<11 mmol/L excluded 99% of DM. A combination of FPG>8.0 mmol/L and 1hPG>15 mmol/L identified 100% of patients with DM. CONCLUSIONS: Based on its satisfactory accuracy to detect DM an algorithm is proposed for screening for DM in patients with CAD decreasing the need for a 2-hour OGTT by 71%.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".