Volume estimation in dialysis patients: The concordance of brain‐type natriuretic peptide measurements and bioimpedance values
Bibliographic record
Abstract
Correct estimation of the dialysis patients' hydration status remains an important clinical challenge. Bioimpedance measurements have been validated by various physiological tests, and the use of brain-type natriuretic peptide (BNP) has been validated by inferior vena cava diameter measurements. This is an observational cohort study that evaluated the correspondence between bioimpedance-measured overhydration percentage (OH%) and BNP. We measured predialysis OH% by bioimpedance apparatus (Body Composition Monitor) and BNP by microparticle enzyme-linked immunoassay in 41 prevalent stable hemodialysis patients, 19 (46%) women, aged 58.9 ± 14.5 years. The cohort's average BNP was 2694 ± 3278 pg/mL and 10 (24.4%) of these 41 patients had BNP < 500 pg/mL (average 260.7 ± 108.5). The OH% was 8.5 ± 7.0% among those with a BNP < 500 pg/mL, while the rest of the population had an OH% of 21.4 ± 8.0%, corresponding to excess volumes of 1.6 ± 1.3 and 4.4 ± 3.8 L, respectively. The OH% vs. BNP relationship was best described by the exponential regression of y = 216.4e(0.097x) , predicting a BNP of 216.4 pg/mL at 0% overhydration status (r 0.61). Receiver-operating curves revealed an area under the curve of 0.885 for BNP when the OH% was set ≥15% of overhydration and an area under the curve of 0.918 for OH% when the BNP was set ≥500 pg/mL for being abnormal. We conclude that in our cohort there was a high degree of correspondence between these two tests with an exponential relationship between the measurements.
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".