Extravascular Lung Water and Peripheral Volume Status in Hemodialysis Patients With and Without a History of Heart Failure
Bibliographic record
Abstract
Determining volume status in hemodialysis patients with a history of congestive heart failure (CHF) is difficult. Extravascular lung water (EVLW) may be derived from blood ultrasound velocity changes following injections of 0.9% and 5% saline. Bioimpedance spectroscopy can measure total body water (TBW) and its intracellular fluid (ICF) and extracellular fluid (ECF) compartments. We studied 29 clinically euvolemic hemodialysis patients, 12 of whom had a history of CHF. The ECF and ICF were measured before dialysis, and EVLW was measured during dialysis. Values of EVLW were similar between patients without CHF and those with CHF (3.55 ml/kg +/- 0.94 SD versus 3.88 ml/kg +/- 0.82 SD, respectively; p = NS). The ECF/ICF ratio was higher among patients with a history of CHF (1.27 +/- 0.29) than among those without such a history (1.04 +/- 0.04; p < 0.05), indicating that ECF volume overload was present in both groups, but was higher in those with a CHF history. There was a positive correlation between EVLW and ECF/ICF ratios (r = 0.54, p < 0.01). Measurements of EVLW were higher in two pulmonary edema patients ((7.95 ml/kg and 5.95 ml/kg; p < 0.05). The results of this study suggest that 1) hemodialysis patients with a history of CHF have more ECF volume overloaded than those without such a history; 2) the degree of ECF expansion is associated with increasing EVLW volume, even in patients without pulmonary edema; and 3) ECF volume expansion eventually exceeds limits and pulmonary edema occurs. These developing technologies of volume measurement may be of value in this challenging clinical area.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".