Comparison of the Multiple-Aliquot and Batch Methods of Monitoring Peritoneal Dialysis Adequacy in Patients
Bibliographic record
Abstract
BACKGROUND: Effluent fluid is analyzed to determine Kt/V urea and creatinine clearance as measures of adequacy of peritoneal dialysis. To avoid the physical mixing of fluids and to minimize handling of full effluent bags, a multiple-aliquot method of sampling was developed and compared to the traditional batch method. METHODS: The batch method and the multiple-aliquot method were performed for 31 consecutive patients. Pooled fluid urea and creatinine measurements were determined for each method. PD Adequest 2.0 (Baxter Healthcare, Deerfield, IL, USA) was used to derive calculated peritoneal dialysis parameters. RESULTS: Urea dialysate levels, calculated weekly urea clearances, protein catabolic rate, and Kt/V were not statistically different (p > 0.05) between the 2 methods. Dialysate creatinine and creatinine clearance with the 2 methods were statistically distinct but the differences were not clinically important. The processing time per set of patient effluent bags was reduced from 45 to 18 minutes, handling of the bags was minimized, and error associated with inadequate mixing of pooled fluids was avoided. CONCLUSION: The multiple-aliquot method generates accurate and timely results to assess peritoneal dialysis prescription adequacy while reducing staff effort.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".