DAILY HEMODIALYSIS—SELECTED TOPICS: Predicting Treatment Dose for Novel Therapies Using Urea Standard <i>Kt</i>/<i>V</i>
Bibliographic record
Abstract
Calculation of urea standard Kt/V (stdKt/V) as a dose measure for guiding novel hemodialysis or hemofiltration therapy prescription is complex since this parameter depends on the magnitude of posttreatment urea rebound. We propose here a two-step procedure for calculating urea stdKt/V from single-pool urea Kt/V values (spKt/V) determined from serum urea concentrations in pretreatment and posttreatment blood samples. First, the dependence of urea stdKt/V on equilibrated Kt/V (eKt/V) was derived from a fixed-volume single-pool model. Second, an empirical equation for predicting urea eKt/V from urea spKt/V values was determined using multiple linear regression and available data during 4-hour hemodialysis, 2-hour hemodialysis, and 2-hour hemofiltration treatments. This empirical (rate/dose) equation is likely more robust for novel therapies than other equations derived from only data during conventional (4-hour) hemodialysis treatments. The combination of these formulas allowed construction of nomograms for calculating urea stdKt/V from spKt/V during novel therapies. These principles were further illustrated by calculating the predicted treatment dose for daily (six times per week) hemofiltration therapy required to achieve a given urea stdKt/V.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 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 teacher head, 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".