Identifying Reliable Biomarkers for Pulmonary Congestion: Toward a Close Yet Sustainable Heart Failure Follow-Up
Bibliographic record
Abstract
This article refers to ‘Serial cardiac biomarkers, pulmonary artery pressures and traditional parameters of fluid status in relation to prognosis in patients with chronic heart failure: Design and rationale of the BioMEMS study’ by Y. Allach et al., published in this issue on pages 1736–1744. Heart failure (HF) affects over 64 million people worldwide, and its burden is expected to increase over the next few years.1 Hospital admissions due to HF not only impact the patients' quality of life and prognosis, but also impose a great burden on healthcare systems. Early detection of impending decompensation may allow for changes in therapeutic strategy and potentially prevent a hospitalization due to worsening HF.2 Haemodynamic congestion is a primary cause of HF hospitalizations, underscoring the need for reliable markers of congestion. The biomarkers used in clinical practice are B-type natriuretic peptides (NPs), which have several limitations. First, several comorbidities (i.e. atrial fibrillation, chronic kidney disease, and obesity) may affect NP values regardless of congestion status.3 Second, measurement intervals during follow-up have not been standardized and typically range from 6 to 12 months. Third, no validated protocols for therapy adjustment based on NP changes exist because of the little evidence on the prognostic efficacy of treatment strategies guided by NP values.4
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".