Residual Red Blood Cell Volume in Extracorporeal Blood Circuit after Hemodialysis: A Single-Center Study
Bibliographic record
Abstract
INTRODUCTION: The factors contributing to blood loss during hemodialysis (HD) procedures remain underexplored. This study aimed to quantify blood loss during HD and identify the potential factors associated with it. METHODS: The study included 70 ESRD patients undergoing HD. After dialysis, the extracorporeal blood circuits were rinsed with 1,000 mL of 0.05% NH3 solution in distilled water, and hemoglobin levels were measured. Univariate regression was used to assess the linear relationship between residual red blood cell (RBC) volume and various parameters, including HD mode, dialyzer surface area, ultrafiltration goal, hypotension during HD, blood flow rate, activated partial thromboplastin time, and C-reactive protein. Multivariate regression was also conducted to explore the relationships among these parameters. RESULTS: The mean RBC volume remaining in the extracorporeal blood circuit after HD was 1.6 ± 0.9 mL (mode: 1.0, range: 0.3-6.5 mL). When converted to whole blood volume per patient, the mean blood volume was 5.3 ± 3.0 mL (median: 4.1 mL, mode: 4.0 mL, range: 1.0-19.0 mL). Multivariate analysis identified the dialyzer surface area as the only significant determinant of residual RBC volume. CONCLUSION: After HD, the remaining RBC volume in the extracorporeal blood circuit varies from 1.6 to 6.5 mL. When the RBC volume was converted to whole blood volume for each case, the blood loss ranged from 1.0 to 19.0 mL. Dialyzer surface area was the only significant determinant of residual RBC volume.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".