Calculation of Standard Kt/V (stdKt/V) with Corrections for Postdialysis Urea Rebound
Bibliographic record
Abstract
Recent work has shown that postdialysis urea rebound and equilibrated Kt/V (eKt/V) for short daily therapies, either hemodialysis (HD; Leypoldt et al, 2002 ASN meeting) or hemofiltration (HF; Jaber et al, this meeting), cannot be predicted by the Daugirdas-Schneditz rate equation. We derived a modified rate equation that accurately predicts eKt/V from single-pool Kt/V (spKt/V) and used it to derive nomograms for calculating stdKt/V from spKt/V for therapies containing an arbitrary number (N) of either short (2 hr) or long (8 hr) treatments per week. The modified rate equation was derived using multiple linear regression from measured spKt/V and eKt/V values during conventional (4 hr) HD (n = 21), short (2 hr) HD (n = 21) and short (2–3 hr) HF (n = 33). Values of stdKt/V were calculated using a fixed-volume kinetic model: stdKt/V = 168*(1-exp-[-eKt/V])/t/((1-exp [-eKt/V])/(eKt/V)+ 168/N/t-1), where t = treatment time in hrs. Measured values of spKt/V ranged from 0.38–1.89 and treatment rate (K/V) from 0.10–0.63/hr. The derived modified rate equation was: eKt/V = 0.927*spKt/V− 0.255*K/V. Prediction of eKt/V from this equation was high (r2 = 0.986, p < 0.0001), and calculated relationships between stdKt/V per week and spKt/V per treatment are as shown in the figures below. We suggest that stdKt/V can be predicted from spKt/V values using a modified rate equation and a fixed-volume kinetic model.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".