Utility of a blood volume monitor in the management of anemia in dialysis by computing the total hemoglobin mass
Bibliographic record
Abstract
Abstract Introduction The degree of interdialytic weight gain and ultrafiltration may influence anemia results in dialysis. The purpose of this study is to evaluate the utility of a blood volume monitor (BVM) in the management of renal anemia and its ability to avoid the variability of hematocrit (Hct) and hemoglobin values (Hb) depending on plasma volume through a simple method of monitoring the total hemoglobin mass (MtHb). Methods Predialysis blood samples for measurement were drawn at both the midweek treatment and the beginning‐of‐the‐dialysis‐week treatment in 30 patients. The MtHb was calculated as MtHb = Vb × Hb, where Vb is the absolute blood volume determined by online dialysate dilution using an online hemodiafiltration machine incorporating a relative BVM. Findings The MtHb and the total red cell volume (VRBC) as measured with the bolus method at the starting of the treatment were 540 ± 148 grams and 1544 ± 339 mL, respectively. There were significant differences between the Hb levels and between the hematocrit levels according to the time of dialysis. However, the MtHb remained constant. There was also an excellent correlation between the Hb measurements by the BVM and the blood sampling method (R = 0.89, P value <0.001). Conclusion Our study suggests that BVM could be very useful in the management of anemia in dialysis by computing the total Hb mass in clinical practice and may support better and more appropriate assessments of the factors influencing circulating Hb.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".