Lung water density is increased in patients at risk of heart failure and is largely independent of conventional cardiovascular magnetic resonance measures
Bibliographic record
Abstract
Abstract Aims Non-invasive methods to quantify pulmonary congestion are lacking in clinical practice. Cardiovascular magnetic resonance (CMR) lung water density (LWD) mapping is accurate and reproducible and has prognostic value. However, it is not known whether LWD is associated with routinely acquired CMR parameters. Methods and results This was an observational cohort including healthy controls and patients at risk of heart failure. LWD was measured using CMR with a free-breathing short echo time 3D Cartesian gradient-echo sequence with a respiratory navigator at 1.5 T. Associations were assessed between LWD, lung water volume and cardiac volumes, left ventricular (LV) mass and function, myocardial native T1, and extracellular volume fraction. In patients at risk for heart failure (n = 155), LWD was greater than in healthy controls (n = 15) (30.4 ± 5.0 vs. 27.2 ± 4.3%, P = 0.02). Using receiver operating characteristic analysis, the optimal cut-off for LWD was 27.6% to detect at-risk patients (sensitivity 72%, specificity 73%, positive likelihood ratio 2.7, and inverse negative likelihood ratio 2.6). LWD was univariably associated with body mass index (BMI), hypertension, right atrial area, and LV mass. In multivariable linear regression, only BMI remained associated with LWD (R2 = 0.32, P < 0.001). Conclusion LWD is increased in patients at risk for heart failure compared with controls and is only weakly explained by conventional CMR measures. LWD provides diagnostic information that is largely independent of conventional CMR measures.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".