Bibliographic record
Abstract
Myocardial fibrosis has a pivotal role in the pathophysiology of heart failure (HF) and may be a major target of novel therapies. These aspects are discussed in a thorough review by Heymans et al.1 The role of increased miRNA-765 as a cause of enhanced protein phosphatase 1 activity and reduced sarcoplasmic reticulum calcium load is shown by Kranias et al.2 Gene therapy with ribonucleoride-reductase has increased levels of the myosin activator 2-deoxy-ATP and enhanced contractility in a suine model of HF.3 The clinical characteristics, treatment and outcomes of patients with HF enrolled in a prospective, hospital-based registry in Trivandrum, Kerala, India, are shown.4 Differences between Arab and Jewish patients with HF living in Jerusalem, Israel, are assessed by Gotsman et al5 and discussed by Hauptman.6 The association between 6-months changes in NT-proBNP levels and outcomes in 2612 participants in the Irbesartan in Patients with HF and Preserved Systolic Function Study (I-Preserve) is shown by Jhund et al.7 A simple validated score for predicting the risk of hospitalization for HF in ambulatory patients is presented by Alvarez-Garcia et al.8 Thromboembolic risk and how to better reduce it remains an unmet need in patients with HF.9 In this issue of the journal, two articles show the thromboembolic risk in 136,545 patients hospitalized for HF in sinus rhythm from large national registries and in patients with hypertrophic cardiomyopathy, respectively.10, 11
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 | 0.007 |
| 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 | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.616 | 0.524 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".