Anemia is an independent risk for mortality after acute myocardial infarction in patients with and without diabetes
Bibliographic record
Abstract
INTRODUCTION: Anemia and diabetes are risk factors for short-term mortality following an acute myocardial infarction(AMI). Anemia is more prevalent in patients with diabetes. We performed a retrospective study to assess the impact of the combination of diabetes and anemia on post-myocardial infarction outcomes. METHODS: Data relating to all consecutive patients hospitalized with AMI was obtained from a population-based disease-specific registry. Patients were divided into 4 groups: diabetes and anemia (group A, n = 716), diabetes and no anemia (group B, n = 1894), no diabetes and anemia (group C, n = 869), and no diabetes and no anemia (group D, n = 3987). Mortality at 30 days and 31 days to 36 months were the main outcome measures. RESULTS: 30-day mortality was 32.3% in group A, 16.1% in group B, 21.5% in group C, 6.6% in group D (all p < 0.001). 31-day to 36-month mortality was 47.6% in group A, 20.8% in group B, 34.3% in group C, and 10.4% in group D (all p < 0.001). Diabetes and anemia remained independent risk factors for mortality with odds ratios of 1.61 (1.41-1.85, p < 0.001) and 1.59 (1.38-1.85, p < 0.001) respectively at 36 months. Cardiovascular death from 31-days to 36-months was 43.7% of deaths in group A, 54.1% in group B, 47.0% in group C, 50.8% group D (A vs B, p < 0.05). INTERPRETATION: Patients with both diabetes and anemia have a significantly higher mortality than those with either diabetes or anemia alone. Cardiovascular death remained the most likely cause of mortality in all groups.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".