Adequacy of hemodialysis in acute kidney injury: Real‐time monitoring of dialysate ultraviolet absorbance vs. blood‐based Kt/Vurea
Bibliographic record
Abstract
BACKGROUND: Current guidelines recommend monitoring the adequacy of hemodialysis (HD) treatments in patients with acute kidney injury (AKI). Blood-based methods for calculating urea such as reduction ratio (URR) and single-pool Kt/Vurea (spKt/Vurea) require pre- and post-HD blood urea nitrogen (BUN) measurements. This study aims to compare real-time monitoring of urea clearance using dialysate ultraviolet absorbance (UV) with laboratory-measured spKt/Vurea. METHODS: We conducted a single-center, retrospective study among hospitalized patients with AKI, who required intermittent hemodialysis (IHD). Those patients whose dialysis dose was simultaneously monitored by spKt/Vurea and UV-absorbance (UV-spKt/Vurea) were included in the study. The statistical correlation between both methods was assessed by means of the Pearson moment product correlation, Mann-Whitney U-test and Bland-Altman analysis of agreement. RESULTS: Thirty patients with AKI were evaluated. There was no statistical difference between the mean spKt/Vurea calculated by traditional methods and the mean UV-spKt/Vurea. (1.37 ± 0.37 vs. 1.28 ± 0.36, P = 0.12, CI: 95%). A Pearson moment correlation analysis revealed a close agreement between both methods (r = 0.79, P < 0.001). Furthermore, Bland-Altman analysis showed that >95% of the data points were confined within the upper and lower levels of agreement. CONCLUSION: In this pilot study of patients with AKI, UV-spKt/Vurea correlated with standard blood-based spKt/Vurea and may be a useful tool to monitor dialysis adequacy. Larger studies evaluating multiple UV and blood-based measurements per patient and a more diverse AKI population are needed to confirm this initial observation.
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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 | 0.006 |
| 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 | 0.001 | 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".