Cardiac remodelling – Part 2: Clinical, imaging and laboratory findings. A review from the Study Group on Biomarkers of the Heart Failure Association of the European Society of Cardiology
Bibliographic record
Abstract
In patients with heart failure, the beneficial effects of drug and device therapies counteract to some extent ongoing cardiac damage. According to the net balance between these two factors, cardiac geometry and function may improve (reverse remodelling, RR) and even completely normalize (remission), or vice versa progressively deteriorate (adverse remodelling, AR). RR or remission predict a better prognosis, while AR has been associated with worsening clinical status and outcomes. The remodelling process ultimately involves all cardiac chambers, but has been traditionally evaluated in terms of left ventricular volumes and ejection fraction. This is the second part of a review paper by the Study Group on Biomarkers of the Heart Failure Association of the European Society of Cardiology dedicated to ventricular remodelling. This document examines the proposed criteria to diagnose RR and AR, their prevalence and prognostic value, and the variables predicting remodelling in patients managed according to current guidelines. Much attention will be devoted to RR in patients with heart failure with reduced ejection fraction because most studies on cardiac remodelling focused on this setting.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".