Bibliographic record
Abstract
The equilibrated Kt/V (eKtV) is widely used in hemodialysis (HD) as a measure of the intensity (magnitude) of an individual dialysis treatment. Adequate eKt/V for thrice‐weekly hemodialysis (twHD) has been extensively studied, and a value in the range 1.0 – 1.1 per treatment (3.0 – 3.3 weekly) is generally considered to represent adequate therapy for this specific frequency of dialysis. However, for other schedules, summing eKt/V's and time‐averaging the clearance is not appropriate. This was first demonstrated several years ago by the observation that a weekly eKt/V of 2.0 in continuous ambulatory peritoneal dialysis (CAPD) is therapeutically equal to a weekly eKt/V of 3.0 in twHD. That paradox has been resolved by the standard Kt/V (stdKt/V), which accounts for the first order nature of solute removal by dialysis, and which correctly predicts a normalized weekly stdKt/V of 2.0 for both CAPD and twHD. The equivalent renal clearance (EKR) has also been advanced as a method to normalize dose for varying treatment schedules. However, mathematical consideration shows that EKR is an exact time‐averaged clearance. Analysis of data reported for daily dialysis by Piccoli et al. in the present issue of Hemodialysis International shows that the EKRct/V calculated for daily dialysis is identical to the sum of eKt/V's for the individual dialyses. We therefore conclude that EKR is not a suitable parameter for normalizing the dialysis dose, because it fails to reflect the effect of dialysis frequency in HD therapy.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".