106 Patient‐years experience with the Aksys PHD System for quotidian home hemodialysis
Bibliographic record
Abstract
The Aksys PHD System, designed to utilize ultrapure dialyzate for quotidian hemodialysis at home, uses mechanical cleaning and hot water sanitization of the blood, dialysate, and water flow‐paths from inlet to outlet. Since January 2000, it has been used by 110 US patients and 8 UK patients for a total of 106 patient years and more than 30,000 dialyses runs. Of those treated, 75 patients were male and 43 female; mean age was 52 ± 25 (range 22–82) years; 65% were white, 25% black, and 10% other; mean weight was 78 ± 20 (44–125) kg; the cause of renal failure was primary renal disease (50%), hypertension (24%), diabetes (19%), and other (4%). Dialysis access included fistula (61%), graft (25%), and catheter (14%). Patients had been on ESRD therapy on average of 6 ± 7 (0–32) years when starting on PHD dialysis. As of August 2004, patients had dialyzed 11 ± 8 (1–52) months on the PHD. Of those, 78 patients remained on the PHD, 12 were transplanted, 10 died, 7 returned to conventional dialysis at the end of the original study for the FDA and 7 for medical or social reasons, 2 returned to quotidian dialysis on other equipment, and 2 stopped during home dialysis training. Patients dialyzed an average of 145 ± 27 min, 5.6 ± 0.6 dialyses/week with a QB of 376 ± 45 ml/min and a QD of 545 ± 170 ml/min. eKt/V was 0.68 ± 0.20 and weekly stdKt/V was 2.61 ± 0.52. Mean dialyser reuse was 17 ± 14 times without significant decline in urea clearance. 23/118 patients (19%) who came to the PHD from quotidian dialysis on other equipment thought the PHD twice as easy to use and experienced only half as many episodes hypotension, cramps, headache, backache, nausea, and arrhythmias (all p < 0.02). They were hospitalized only half as many days on the PHD. Cumulative patient survival was 60% at 4 years, with 94 deaths/1,000 patient years, relative risk 0.56 compared with age‐matched patients from the USRDS database. Conclusion: This large clinical experience shows the PHD System is easier to use and delivers smoother dialysis with better cardiovascular stability than conventional dialysis machines. It easily fulfills the DOQI guidelines for adequacy of dialysis, economizes on use of dialyzers, tubing, and dialysate, results in less hospitalization, and appears to result in superior patient survival.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".