Comparative Study between Classic and Newer Methods for the Evaluation of Hemodialysis Adequacy
Bibliographic record
Abstract
Aim: The comparative study of hemodialysis (HD) adequacy of Kt/V measurement between classic method (Daugirdas formula) and urea sensor monitor (online). Patients and methods: 30 patients with end‐stage renal failure undergoing dialysis were studied. A comparative evaluation of HD adequacy during the same session was done with two different methods: (1) blood samples were drawn in the beginning and in the end of HD session for the measurement of blood urea nitrogen (BUN) and after measurement of HD adequacy by 3rd generation Daugirdas formula and (2) urea sensor monitor use for continuous HD adequacy measurement during HD session. Results: There was statistically significant correlation of Kt/V Daugirdas with Kt/V online (r = 0.8, p < 0.001). Also there was statistically significant correlation between solute removal index (SRI), Kt/V Daugirdas (r = 0.81, p < 0.001) and Kt/V online (r = 0.92, p < 0.001). From nutrition indices that were measured, the protein catabolic rate (PCR) had marginal negative correlation with the two compared adequacy indices, Kt/V Daugirdas (r = −0.24, p < 0.03), and Kt/V online (r = −0.17, p < 0.03) although the nPCR (normalized PCR) had marginal positive correlation (r = 0.35, p < 0.05) (r = 0.42, p < 0.05). Conclusions: The use of online urea sensor monitors contributes to the easy measurement of adequacy and nutrition indices and hence complicated mathematical formulas are not necessary. The results of these measurements are reliable and comparable with classic methods of HD adequacy evaluation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".