Advances in sorbent peritoneal dialysis technologies: A narrative review
Bibliographic record
Abstract
Sorbent peritoneal dialysis (SPD) removes a tidal volume of spent dialysate, passing it through sorbent layers before infusing replacement electrolytes and dextrose to regenerate dialysate. We examine the five devices using SPD in published literature, reviewing their design, dialysis clearance, and ultrafiltration (UF) capacity-Automated Wearable Artificial Kidney (AWAK), now called Viva Kompact since 2024, Carry Life System PD/Carry Life UF, Wearable Artificial Kidney (WEAKID), Vicenza Wearable Artificial Kidney (ViWAK), and Renart-PD. Carry Life devices and Viva Kompact have reported on human trials, WEAKID and Renart-PD on animal studies, while ViWAK has published in vitro data. All devices have published data on dialysis clearance capabilities. WEAKID and Carry Life PD achieved a high dialysate:plasma concentration gradient for small solutes. Viva Kompact and Renart-PD reported stable or lower serum concentrations of urea, creatinine, phosphate, and β2-microglogulin following treatments. ViWAK demonstrated removal of creatinine, B2 microglobulin, and angiogenin to <10% of pre-treatment levels. UF capacity remains unknown for many devices. In human trials, Carry Life UF has achieved 863 mL UF in a 5-h treatment with the addition of 20 g/h of glucose to 1.5% dextrose dialysate. Viva Kompact has demonstrated 877 mL UF in a 9-h treatment using 1.5% dextrose dialysate in an animal model, comparable to 10-h APD, with the addition of 6.6 g/h glucose. Both devices have demonstrated improved UF per gram of glucose used. The expected use of these devices varies greatly, from an adjunct to currently available treatments to a complete replacement for current modalities. Large-scale, human studies are needed to determine their role in the future of PD delivery.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".