Relationship of dysglycemia to acute myocardial infarct size and cardiovascular outcome as determined by cardiovascular magnetic resonance
Bibliographic record
Abstract
BACKGROUND: Improved outcomes for normoglycemic patients suffering acute myocardial infarction (AMI) over the last decade have not been matched by similar improvements in mortality for diabetic patients despite similar levels of baseline risk and appropriate medical therapy. Two of the major determinants of poor outcome following AMI are infarct size and left ventricular (LV) dysfunction. METHODS: Ninety-three patients with first AMI were studied. 22 patients had diabetes mellitus (DM) based on prior history or admission blood glucose ≥ 11.1 mmol/l. 13 patients had dysglycemia (admission blood glucose ≥ 7.8 mmol/l but < 11.1 mmol/l) and 58 patients had normoglycemia (admission blood glucose < 7.8 mmol/l). Patients underwent cardiac magnetic resonance (CMR) imaging at index presentation and median follow-up of 11 months. Cine imaging assessed LV function and late gadolinium contrast-enhanced imaging was used to quantify infarct size. Clinical outcome data were collected at 18 months median follow-up. RESULTS: Patients with dysglycemia and DM had larger infarct sizes by CMR than normoglycemic patients; at baseline percentage LV scar (mean (SD)) was 23.0% (10.9), 25.6% (12.9) and 15.8% (10.3) respectively (p = 0.001), and at 11 months percentage LV scar was 17.6% (8.9), 19.1% (9.6) and 12.4% (7.8) (p = 0.017). Patients with dysglycemia and DM also had lower event-free survival at 18 months (p = 0.005). CONCLUSIONS: Patients with dysglycemia or diabetes mellitus sustain larger infarct sizes than normoglycemic patients, as determined by CMR. This may, in part, account for their adverse prognosis following AMI.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".