Comparison between lung ultrasonography and current methods for volume assessment in Asian chronic hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Volume assessment in end-stage kidney disease patients on hemodialysis (HD) remains inadequate by existing methods: clinical examination, bioimpedance spectroscopy, measurement of inferior vena cava diameter by ultrasound (IVCD), and plasma B-type natriuretic peptide (NT-pro BNP). This study aims to compare the performance of lung ultrasound against existing methods for volume assessment in a HD cohort. METHODS: Two nephrologists independently performed 28-point lung ultrasound immediately before and after midweek HD in 50 patients. Lung congestion was classified into mild, moderate, and severe categories based on lung ultrasound findings. Clinical examination for crepitations and oedema, change in hydration status (∆HS) measured by bioimpedance spectroscopy, NT-pro BNP, IVCD during inspiration (IVCDimin), expiration (IVCDimax), and inferior vena cava collapsibility index were also assessed before and after midweek HD. FINDINGS: In all, 61% of patients with normohydration status by bioimpedance spectroscopy had moderate or severe lung congestion on lung ultrasound. There were significant correlations between predialysis lung ultrasound, and NT-pro BNP (r = 0.432, P = 0.004), ∆HS (r = 0.447, P < 0.001), and IVCD parameters (P < 0.05). Some correlations weakened postdialysis (∆HS [r = 0.322, P = 0.01] and IVCDimax [r = 0.307, P = 0.03]), whereas NT-pro BNP and ∆HS paradoxically increased in 28% and 30% of the cohort, respectively. On receiver operator curve analysis, most methods of volume assessment had limited discriminatory power to detect mild lung congestion. DISCUSSION: Lung ultrasound demonstrates some comparability with existing volume assessment methods in Asian dialysis patients. However, it appears more effective at detecting subclinical pulmonary congestion, and tracking fluid changes real-time compared to bioimpedance spectroscopy and NT-pro BNP.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".