Updates in Heart Failure: What Last Year Brought to Us
Bibliographic record
Abstract
Heart failure (HF) is common, and HF with preserved ejection fraction (HFpEF) has become its most frequent clinical presentation because of ageing of the population and decreasing prevalence of coronary artery disease (CAD).1,2 An analysis of all studies using echocardiography to estimate the prevalence of cardiac dysfunction in subjects aged ≥60 years showed a median prevalence of 36.0%(range 15.8-52.8%)and 5.5% (range 3.3-9.2%)for 'isolated' left ventricular (LV) diastolic dysfunction and LV systolic dysfunction, respectively, and a median prevalence of 4.9% (range 3.8-7.4%)and 3.3% (range 2.4-5.8%) for symptomatic HFpEF and HF with reduced ejection fraction (HFrEF).3 Outcomes Outcomes of patients with HF remain poor.The European Society of Cardiology (ESC) HF Long-Term Registry collected data of 12 440 patients with HF, 59.5% outpatients and 40.5% hospitalized patients for acute HF (AHF), enrolled from 211 cardiology centres in 21 European and/or Mediterranean countries.4 The 1 year all-cause mortality rate was 6.4% for ambulatory patients and raised to 23.6% for those hospitalized for AHF.The combined endpoint of 1 year mortality or HF hospitalization occurred in 14.5% of outpatients and 36% of hospitalized patients.A primary care-based cohort study in Scotland compared the 5 year survival of patients with HF with that of the most common causes of cancer.The 5 year survival of patients with HF was of 55.8% in men and 49.5% in women, and it was better than that of patients with lung cancer, colorectal cancer, and, in women, ovarian cancer but worse than that of male patients with prostate cancer and of female patients with breast cancer.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.031 | 0.024 |
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".