New diagnosis of diabetes in patients with myocardial infarction or stroke: a systematic review and meta-analysis
Bibliographic record
Abstract
AIMS: What proportion of patients with an acute myocardial infarction (MI) or stroke get a new diagnosis of diabetes (NDD) at the time of hospitalization is unclear. METHODS AND RESULTS: We systematically searched MEDLINE and Embase from database inception to 30 January 2025, to select English-language observational studies that included adult patients with MI or stroke and reported the number of patients with NDD. The denominator was patients without known diabetes. Study quality was assessed using the Joanna Briggs Institute checklist. Random-effects meta-analyses were used to calculate the pooled proportion of NDD in patients with MI or stroke. Heterogeneity was explored in subgroup analyses, and any change over time was evaluated using meta-regression. 82 studies that included 9440 patients with NDD were identified. Included studies were of good methodological quality: 17 (22%) tested all eligible patients for diabetes, and 19 (23%) used registry-based samples. 16.0% [95% confidence interval (CI), 14.3-17.7, n = 52] patients with MI had NDD, and 15.3% (95% CI 12.5-18.0, n = 30) patients with stroke had NDD, albeit with high heterogeneity. The pooled proportion was higher when oral glucose tolerance test was used to diagnose diabetes. The proportion of NDD did not change over the last 30 years (0.1% per year decline; 95% CI -0.3% to 0.1%). CONCLUSION: Among patients admitted with MI or stroke, one in six gets a new diagnosis of diabetes. Improving diabetes screening could help identify people before having a diabetes-related cardiovascular event.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".