Use of ionic dialysance to calculate <scp>Kt/V</scp> in pediatric hemodialysis
Bibliographic record
Abstract
Online clearance (OLC) monitor measures conductivity difference between dialysate entering and leaving the dialyser. Derived ionic dialysance (ID) represents effective urea clearance from which Kt/V is calculated, allowing Kt/V monitoring at every treatment without blood sampling. We tested ID accuracy in children and provide recommendations for its use. Using Fresenius machines 2008 K with built-in OLC monitors, we studied 45 hemodialysis (HD) sessions and 168 calculated Kt/V results in 11 patients. Urea distribution volume (V), needed to calculate Kt/V from ID, was estimated using three methods: Mellits and Cheek (MC), KDOQI recommended total body water nomograms (TBWN) and OLC-derived independent from tested HD sessions. Reference spKt/V from pre- and post-HD BUN (Daugirdas) was compared with Kt/V calculated from ID using three different estimated V's. ID was accurate in calculating Kt/V in children when V derived from OLC was used (P = 0.42), with absolute error 0.14 ± 0.12. If TBWN-derived V was used, Kt/V was consistently underestimated by 0.32 ± 0.22. TBWN-derived V can still be recommended for use with OLC for monitoring trend in Kt/V, if underestimation of spKt/V of average 0.3 is accounted for. MC-derived V results in even greater underestimation of spKt/V and therefore cannot be recommended for use with OLC.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".