Heart Failure and Nephropathy
Bibliographic record
Abstract
Heart failure (HF) is a major contributor to poor quality of life, a leading cause of hospitalization, and cause of premature death. Both kidney disease and diabetes are major and independent risk factors for the development of heart failure, such that individuals with diabetic nephropathy are at especially high risk. Such patients not only are likely to have coronary artery disease and hypertension but also are likely to have diabetic cardiomyopathy, a distinct pathologic entity that is more closely associated with the microvascular than the macrovascular complications of diabetes. In addition to a better understanding of the epidemiology of HF, advances in noninvasive imaging have highlighted the importance of early cardiac dysfunction in diabetes and the high prevalence of HF with preserved left ventricular systolic function. Although significant renal dysfunction is usually an exclusion criterion in HF trials, diabetes is often a prespecified subgroup so that subanalyses of large multicenter clinical trials do provide some guidance in therapeutic decision-making. However, further therapies for both HF and nephropathy in diabetes clearly are needed, and a number of new therapeutic strategies that target both disorders have already entered the clinical arena.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".