Bibliographic record
Abstract
Much is dedicated to cardiovascular imaging in heart failure (HF) in this issue. Ferre et al. review the role of cardiopulmonary ultrasound in the diagnosis and early management of acute HF. A stepped approach is shown where the role of simple imaging techniques for the diagnosis of pulmonary and systemic venous congestion, the estimate of the left ventricular filling pressure and the differentiation of the clinical presentations of HF is shown.1 In a research by Ikonomidis et al., impaired left ventricular twisting and untwisting has been related with reduced coronary flow reserve, vascular dysfunction and markers of increased collagen synthesis in patients with hypertensive heart disease.2 A large space is once again dedicated to biomarkers. Much has been written regarding the heterogeneity among the patients with HF and preserved ejection fraction.3 In this issue of the journal, D'Elia et al. discuss the role of new biomarkers for phenotyping these patients and differentiate pathogenetic mechanisms.4 Other studies regard the independent prognostic role of high sensitivity troponin T measurements and their changes in patients with acute HF,5 the use of biomarkers to select the HF patients at low risk of events,6 the selection of the patients more likely to benefit from natriuretic peptides guided therapy7 and a new, and first, marker of muscle wasting.8 With respect of treatment, a simple treatment of sleep disordered breathing, based on a lateral sleep position, is shown to be effective in patients with HF, above all when obstructive sleep apnea is their main complaint.9 The implications of these findings and their comparison with the results of the large randomized trial SERVE-HF are discussed in an accompanying editorial.10 Finally, the design of an innovative trial comparing usual care with the use of the HFA website, heartfailurematters.org, and with an interactive platform including a link to this website, is shown.11 Enjoy reading!
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.486 | 0.417 |
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".