Complementary parameter for dialysis monitoring based on UV absorbance
Bibliographic record
Abstract
An optical on-line monitoring system aimed at the estimation of dialysis dose has been tested clinically. The natural logarithmic slope is used to calculate Kt/V(urea) from ultraviolet (UV)-absorbance measurements. Errors in the calculation of Kt/V(urea) may appear due to changes in blood and dialysate flow or due to disturbances when the slope is used to estimate dialysis dose. This study introduces a new parameter for dialysis monitoring that may be used as a complementary parameter, the area under UV-absorbance curve (AUCa), to reflect a total solute removal during dialysis. The aim was to investigate the relationship between this new dialysis on-line monitoring parameter, AUCa, and the total removal of a few solutes. Fifteen patients were monitored during hemodialysis using UV absorbance at the wavelength of 297 nm. All spent dialysate passed through a flow cuvette in a spectrophotometer and then further to a collection tank where solute concentrations in the entire spent dialysate were determined. The AUCa at 297 nm was compared with the total amount of removed solute in the tank (reference method). The result shows strong correlations between AUCa and the total removal of urea, urate, creatinine, and phosphate during a given treatment and less strong correlation in all 15 patients together. A first indication of a new, possible, complementary parameter in hemodialysis treatment is presented, the AUCa, prospected to estimate solute removal.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".