Thirty Days of Maintenance Peritoneal Dialysis Using a Sorbent-Based Automated Wearable Artificial Kidney (AWAK) PD Device in a Porcine Model
Bibliographic record
Abstract
Background: In sorbent-based PD, spent dialysate is processed and clean dialysate is re-introduced into the abdomen. Our aim was to determine if AWAK dialysis can maintain euvolemia and biochemistry for 30 days in a porcine model. Methods: The study was conducted in a 5/6 nephrectomised pig (Sus Scrofa, male). Post nephrectomy, the animal was treated with CAPD (14 weeks) before commencing sorbent-based PD for 7 hour per day with initial fill of 2L 1.5% Dianeal for 30 consecutive days. Thereafter, the animal was maintained on standard of care (SOC) for 3 days (5x2L exchanges over 10 hour APD, with 1L last fill, 2.5% Dianeal). Results: Stable serum concentration of toxins, electrolytes and inflammatory markers were noted during AWAK therapy (Table); with no significant change after switch to SOC. pH of regenerated dialysate was consistent with biocompatible solutions (figure 1) and appropriate change in ultrafiltration was observed in response to glucose infusion (figure 2). Conclusions: AWAK PD therapy successfully treated a CKD animal model for 30 days, without adverse impact on volume status or clinical parameters. Future long-term human studies are needed for device enhancement. Funding: Commercial Support - AWAK Technologies Pte Ltd#last 3 days data of AWAK and SOC compared
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".