September 2018 at a Glance: Co-Morbidities, Heart Failure with Preserved Ejection Fraction and Mineralocorticoid Receptor Antagonists
Bibliographic record
Abstract
Co-morbidities Non-cardiac co-morbiditiesIorio et al. 1 analysed the role of 15 non-cardiac co-morbidities in 2314 outpatients with chronic heart failure (HF).Obesity and hypertension were more prevalent in HF patients with preserved ejection fraction (HFpEF), compared to those with reduced ejection fraction (HFrEF).A similar prevalence was found for other co-morbidities.An increasing number of non-cardiac co-morbidities was associated with a higher risk for all-cause mortality, HF hospitalizations and non-cardiovascular hospitalizations.The co-morbidities contributing to this increased risk were anaemia, chronic kidney disease, chronic obstructive pulmonary disease, diabetes mellitus, and peripheral artery disease with similar results for HFrEF and HFpEF. 1 A similar role of co-morbidities, independent of left ventricular (LV) ejection fraction, was also found in other analyses.2 Chronic kidney disease was more strongly associated with a poorer outcome in HFrEF than in HFpEF in the Swedish HF Registry.3 with a hazard ratio of 1.05 (95% confidence interval 1.03-1.06)per unit increase in E/e ′ for the combined outcome of all-cause
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".