May 2021 at a glance: focus on acute heart failure and heart failure with preserved ejection fraction
Bibliographic record
Abstract
Acute heart failure Imaging and biomarkersCongestion is the main cause of hospitalization for acute heart failure (HF).1,2 It can be detected by biomarkers and imaging tools for the diagnosis, prognostic evaluation and, possibly, to guide treatment, in patients with acute HF. [3][4][5][6][7] Pellicori et al. 8 reviewed ultrasound methods for the detection and quantification of congestion, including imaging of the heart, lungs (B-lines), kidneys (intrarenal venous flow) and venous system (inferior vena cava and internal jugular vein diameter).Organ injury may be a major mechanism of the deleterious effects of congestion in patients with acute HF. 6 Kozhuharov et al. 9 studied patients presenting to the emergency department with acute dyspnoea and showed that plasma levels of cardiac myosin-binding protein C (cMyC), a marker of myocardial injury, were higher in those with acute HF and were a marker of increased risk of all-cause mortality [hazard ratio (HR) 2.19, 95% confidence interval (CI) 1.66-2.89;P < 0.001 for patients above median cMyC concentrations].Congestion relief has an impact on the prognostic value of markers of kidney function.6 In a prospective, single-centre study, 215 patients with acute HF were divided into four profiles based on their estimated glomerular filtration rate (eGFR) (preserved vs. impaired) and spot urine sodium (sodium excreter vs. non-excreter).Both sodium non-excreter profiles were associated with an increased risk of in-hospital worsening HF, use of inotropes and readmissions due to acute HF.The preserved eGFR/non-excreter profile had the highest 1-year mortality and was an independent predictor of poor outcome at multivariable aanalysis.10
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.500 | 0.328 |
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".