September 2023 at a Glance: Focus on Acute Heart Failure and Health Status
Bibliographic record
Abstract
Heart Failure Association clinical consensus statementsAdvanced heart failure (HF) is burdened by an extremely poor prognosis.1,2 Culture, ethnicity, and socio-economic differences should be considered by the health care professionals in the management of patients with advanced HF needing palliative care.A clinical consensus statement from the Heart Failure Association (HFA) of the ESC, the ESC Patient Forum, and the European Association of Palliative Care summarized current evidence and provided a practical guidance.3 The role of different cardiovascular (CV) imaging techniques in the assessment of left ventricular hypertrophy was reviewed in another HFA scientific statement.4 Artificial intelligenceMachine learning procedures have been successfully used to guide diagnosis and stratify prognosis in HF. [5][6][7] Khan et al. 8 summarized the potential applications of artificial intelligence for HF care, including algorithms for establishing an early diagnosis, phenotyping HF with preserved ejection fraction (HFpEF), and stratifying HF disease severity.Exertional hypoxaemia (oxyhaemoglobin saturation <94%) was observed in 136 (25%) of 539 patients with HFpEF and no coexisting lung disease undergoing invasive cardiopulmonary exercise testing with simultaneous blood and expired gas analysis.It was associated with more severe haemodynamic abnormalities and increased mortality.35
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.272 | 0.168 |
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".