Evolving Towards a More Realistic Approach to the Importance of Left Ventricular Ejection Fraction and Sex in Heart Failure and its Therapy
Bibliographic record
Abstract
This article refers to ‘Interactions between left ventricular ejection fraction, sex and effect of neurohumoral modulators in heart failure’ by P. Dewan et al., published in this issue on pages 898–901. Heart failure (HF) is a multifaceted syndrome accounting for a high rate of death and morbidity worldwide. The approach to this disease has traditionally been based on the evaluation of left ventricular ejection fraction (LVEF), with patients having HF with either reduced (HFrEF, LVEF < 40%), preserved (HFpEF, LVEF > 50%) or mid-range (HFmrEF, LVEF 40–50%) ejection fractions, each of these groups being considered distinct syndromes. Initially, the focus was on patients with HFrEF as these patients were easily identified and known to be at high risk of poor outcomes. However, as our populations age, the profile of patients with HF is evolving and the proportion of patients with HF having better LVEFs (i.e. those with HFpEF and HFmrEF) is increasing.1 Data from the Swedish Heart Failure Registry would suggest that, of patients with HF, 56% have HFrEF, 21% have HFmrEF, and that 23% have HFpEF.2 The characteristics of patients have been found to vary according to LVEF, with age and the proportion of women increasing with increasing LVEF. However, the similarities among patients, regardless of LVEF, are greater than the differences, and we now know that all patients with HF are at high risk of morbidity and mortality regardless of LVEF2,3.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.030 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".