Bibliographic record
Abstract
Most patients (65% to 80%) diagnosed with type 2 diabetes will die from heart disease. Heart disease in patients with diabetes includes several features such as diffusely severe atherosclerosis, associated hypertension, deleterious hyperglycemia with increased endothelial dysfunction, microvascular disease, glycation of proteins, autonomic neuropathy, and abnormal cardiac structure and function. Because heart disease is the major cause of mortality in patients with diabetes, early detection of altered cardiac function is important to improve medical intervention and outcome. Nevertheless, the pathogenesis and pathophysiology of diabetic cardiomyopathy remain unclear and are probably multifactorial.1–3 Preclinical manifestations of diabetic cardiomyopathy may not be significant in daily life activities but may impair maximal exercise capacity.4 Govind et al5 investigated the influences of hypertension, diabetes, and its combination on left ventricular systolic and diastolic functional reserve using tissue Doppler echocardiography (TDE) during dobutamine stress echocardiography (DSE) in subjects with a negative standard DSE (in search of coronary artery disease) in subjects from the Myocardial Doppler in Diabetes (MYDID) study cohort. They found that global left ventricular myocardial systolic reserve was depressed in patients with hypertension and those with diabetes compared to controls. Moreover, the coexistence of both diseases seems to have an additive harmful impact on left ventricular functions. This study provides more evidence of the existence of a diabetic cardiomyopathy.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".