Bibliographic record
Abstract
Daily hemofiltration (D‐HF) is a new treatment modality that shows unique solute removal characteristics and possibly provides high quality of life for patients with end‐stage renal disease. Objective: To evaluate solute‐removal characteristics of D‐HF by kinetic modeling analysis. Methods: Five HD patients with normal 4 h × 3 times/week were switched to D‐HF (2 h × 6 times/week). Ultrafiltration rates (QF) or small solute clearances were approximately 100 mL/min. All the necessary kinetic parameters were determined from patients' physical data and HD portion of the clinical measurements. The two‐compartment kinetic model predicted the concentration changes after switching from normal HD to D‐HF. Results: Concentrations of small solutes such as urea–nitrogen (UN) increased, whereas that of β2‐microglobulin (β2‐MG) decreased after switching from normal HD to D‐HF (Figure 1). Predicted solute concentrations for UN as well as β2‐MG correlated well with the clinical results. The model predicted that QF = 140 mL/min may be required for time‐averaged concentration (TAC) of UN to be unchanged. The model also predicted that the 7‐times/week D‐HF may not increase the TAC of UN very much even after switching from normal HD to D‐HF. Conclusion: D‐HF is suitable for removing larger solutes but may not be good enough for removing small solutes. A 7‐day treatment (7 times/week) may greatly improve the solute removal capacity of the so‐called daily treatment (6 times/week) not only for larger solutes but also for small solutes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".