Examining Ultrafiltration Variance across Two Peritoneal Dialysis Prescriptions of the Automated Wearable Artificial Kidney (AWAK) in a Porcine Model
Bibliographic record
Abstract
Background: AWAK PD uses sorbent-based tidal therapy that regenerates spent dialysate into clean dialysate fluid that returns to the peritoneum. Our aim was to study the ultrafiltration (UF) generation per gram of glucose exposed and absorbed (UF efficiency – UFE) under 2 AWAK PD prescriptions. Methods: This study was conducted in a 5/6 nephrectomised porcine (Sus Scrofa, male). The animal (Low transporter; D/Pcreatinine at 4 hour = 0.40) was maintained on automated PD therapy (5 exchanges of 2L fills of 1.5% Dianeal® over 10 hours) in between the test periods. During the test period, daily 9-hour AWAK PD therapy was conducted; it consists of a 7-hour tidal sorbent phase and a 2-hour non-sorbent dwell phase (initial fill 2L, 1.5% Dianeal®). Glucose was dosed using AWAK’s glucose management system Setting 3; 4 settings are available, ranging from 0.3 – 6.8mL of Glucose 70%, every 7.5 minutes in the first 7 hours of tidal dialysis and during the 2-hour non-sorbent dwell, glucose was dosed every 7.5 minutes (Test A) or 15 minutes (Test B). The animal also had daily last fills (1L, 2.5% Dianeal®) throughout the study period. Post-AWAK PD therapy dialysate data were collected for glucose analysis and UF calculation. Results: The difference in UF between the 2 tests was statistically insignificant (p-value = 0.81). The UF ranges and UFE rates are shown in Table 1. Higher UFE (exposed and absorbed) were observed for Test B and would be a preferred treatment prescription as it was able to achieve similar UF with lowered glucose exposure. This experiment was performed on a porcine model with a low transport status and the results may vary for a high transport status peritoneal membrane. Conclusion: With a reduced requirement of glucose, the device size can be improved. Further long-term studies in both animals and humans are needed to ascertain the efficacy of the glucose management system and prescription implications of AWAK PD therapies. Funding: Commercial Support - AWAK Technologies Pte Ltd Table 1: Summary of UF results from 2 glucose prescriptions - Glucose dosing in non-sorbent phase Test A - every 7.5 minutes [n = 14 days] Test B - every 15 minutes [n = 14 days] Average UF volume (mL); min-max 852 (413 – 1046) 867 (448 – 1064) UFE – exposed (mL/g) 9.4 10.3 UFE – absorbed (mL/g) 18.0 19.2
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".