Bibliographic record
Abstract
BACKGROUND: Daily hemofiltration (D-HF) is a new treatment modality that shows unique solute removal characteristics and possibly provides a high quality of life for patients with end-stage renal disease. We evaluated solute removal characteristics of D-HF for five patients by kinetic modeling analysis. METHODS: Five patients treated with normal 3 x 4 hr/week hemodialysis (HD) were switched to D-HF (6 x 2 hr/week). Ultrafiltration rates (Q(F)) or small-solute clearances ranged from 63 to 106 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 beta(2)-microglobulin (beta(2)-MG) decreased after switching from normal HD to D-HF in all five patients. Predicted solute concentrations and clinical measurements for UN and beta(2)-MG were in good agreement with mean error less than 10%. The model predicted that Q(F) = 155 mL/min may be necessary for time-averaged concentration (TAC) of UN to be unchanged. The model also predicted that the 7 times/week D-HF should not increase the pretreatment concentration of UN, expecting even much lower beta(2)-MG concentration after switching from normal HD to D-HF. CONCLUSION: D-HF is superior to normal HD for removing larger solutes but may increase the TAC of small solutes. Seven-day (7 times/week) D-HF may improve the solute removal capacity of 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".