Bibliographic record
Abstract
BiomarkersBiomarkers are not only an invaluable tool for the diagnosis and prognosis of heart failure (HF), they also have a key role in understanding its pathophysiology.This is also shown by two reviews in this issue of our journal. Circular RNAsSmall non-protein coding RNAs, micro-RNAs, are extensively studied in HF.Different patterns of mRNA levels have been found between HF patients with reduced ejection fraction (HFrEF) and those with preserved ejection fraction (HFpEF).1,2 Changes in mRNA levels are also related to the clinical presentation and prognosis of patients with acute HF. 3 Less data are available regarding long on-coding RNAs and, namely, circular RNAs (circRNAs).These are long non-coding RNAs that can be found both in the cytoplasm and the nucleus of the cells, where they regulate gene expression, as well as in the bloodstream, where they can be measured.In their fascinating article, Devaux et al. review the biogenesis of myocardial circRNAs, their role in the normal and failing heart and their value as biomarkers and as potential therapeutic targets in patients with HF. 4 Medical treatmentCowie et al. summarize the content of an ESC-HFA organized workshop focused on the European Medicines Agency (EMA) revision of their guideline on clinical investigation of medicinal products for the treatment of HF.Endpoint selection, statistical analysis, clinical trial design, research approaches for testing novel therapeutic tools, such as cell therapy, are extensively discussed.15
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 | 0.005 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.090 | 0.058 |
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".