Expert Consensus Document: Reporting Checklist for Quantification of Pulmonary Congestion by Lung Ultrasound in Heart Failure
Bibliographic record
Abstract
Lung ultrasound is a useful tool for the assessment of patients with both acute and chronic heart failure, but the use of different image acquisition methods, inconsistent reporting of the technique employed and variable quantification of 'B-lines,' have all made it difficult to compare published reports. We therefore need to ensure that future studies utilizing lung ultrasound in the assessment of heart failure adopt a standardized approach to reporting the quantification of pulmonary congestion. Strategies to improve patient care by use of lung ultrasound in the assessment of heart failure have been difficult to develop. In the present document, key aspects of standardization are discussed, including equipment used, number of chest zones assessed, the method of quantifying B-lines, the presence and timing of additional investigations (e.g. natriuretic peptides and echocardiography) and the impact of therapy. This consensus report includes a checklist to provide standardization in the preparation, review and analysis of manuscripts. This will serve as a guide for investigators and clinicians and enhance the quality and transparency of lung ultrasound research.
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 | 0.284 | 0.451 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.015 | 0.008 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.012 | 0.008 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".