Differentiating diastolic dysfunction from classic heart failure
Bibliographic record
Abstract
Heart failure (HF) is characterized by an impaired ability of the ventricle to fill with and eject blood commensurate with the metabolic demands and requirements of the body, and has emerged as a major cause of morbidity and mortality in the developed world. Approximately 50% of HF patients have preserved ejection fraction, and lack overt signs of systolic failure, but rather have deficits in ventricular relaxation indicative of diastolic dysfunction/HF. Systolic HF and diastolic dysfunction/HF share a number of risk factors including age, myocardial ischemia, hypertension, obesity, and diabetes. As such these risk factors may be of limited utility in discriminating between the two HF phenotypes. However, differences in ventricular remodeling and ventricular function can readily differentiate systolic HF from diastolic dysfunction/HF. To date, a common modality to improve the status of both HF entities remains to be found. As therapeutic interventions that optimize cardiac energy metabolism have proven useful in the management of systolic HF, and because ventricular relaxation is an energy-dependent process, a better understanding of the alterations in energy metabolism in diastolic dysfunction/HF may identify novel treatment strategies for this cardiac pathology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".