Measurement of Blood Volume During Hemodialysis is a Useful Tool to Achieve Safely Adequate Dry Weight by Enhanced Ultrafiltration
Bibliographic record
Abstract
Chronic fluid overload and hypertension are highly prevalent in the dialysis population. Measurement of blood volume (BV) during hemodialysis (HD) may prove useful to achieve dry weight (DW). Twelve (12) chronic hemodynamically stable dialysis patients were randomly selected to participate in a pilot study. BV changes were measured using an online blood volume monitor (Hemoscan, Gambro AB, Stockholm, Sweden). As part of an initial observation phase, the magnitude of BV variation (deltaBV) in percentage and total UF volume (UF) in liters were recorded for each dialysis session, and the deltaBV/UF ratio was calculated. DW was subsequently reduced by 0.5 kg in all patients and the tolerance of the procedure was assessed. Attempted DW reduction was successful in seven patients, whereas it resulted in hypotension or symptoms in the other five cases. The deltaBV/UF ratio was found to be significantly lower in patients in whom attempted DW reduction was successful (2.47%/L vs. 3.45%/L, P < 0.05). Using receiver operating characteristic (ROC) curve analysis, a deltaBV/UF ratio of less than 2.6%/L offered the best overall prediction of successful DW reduction. These results suggest that measurement of BV changes during HD and calculation of the deltaBV/UF ratio are valuable tools for management of DW in clinically stable patients.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".