Determining Lung Water Volume in Stable Hemodialysis Patients
Bibliographic record
Abstract
Lung water (LW) reflects the water content of the lung interstitium. Because hemodialysis patients have expanded total body water (TBW) they may also have increased LW. Hypertonic saline promotes a flux of water from lung to blood, which is measured by ultrasound flow probes on hemodialysis tubing. The volume of flux is an indirect measure of LW. Our purpose was to determine the feasibility and reproducibility of LW derived with ultrasound velocity dilution, to determine the effect of ultrafiltration on LW in stable hemodialysis patients, and to compare changes in LW with fluid compartment shifts using bioimpedance. Lung water, cardiac output, total body water, and extracellular and intracellular fluid volumes were measured in 24 stable hemodialysis patients at the beginning of hemodialysis and after ultrafiltration. The LW values at the beginning of hemodialysis (298.8 +/- 90.2 ml or 3.67 +/- 1.47 ml/kg) fell during hemodialysis (250.8 +/- 55.8 ml or 3.12 +/- 0.96 ml/kg; p < 0.05), as did TBW and extracellular fluid volumes (p < 0.001). Cardiac output, cardiac index, and central blood volume also decreased significantly with ultrafiltration (p < 0.005, p < 0.005, and p < 0.01, respectively). Results showed that stable hemodialysis patients have higher specific LW values (3.67 ml/kg) than the normal population (2 ml/kg) and ultrafiltration produces a significant decline in LW values.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".