A Report on Home Hemodialysis Training Time in Patients With Kidney Failure Using the Quanta SC+ Hemodialysis Device
Bibliographic record
Abstract
Background: Frequent self-care hemodialysis (HD) in the home setting has several advantages to patients and providers including improved health outcomes, health-related quality of life, patient satisfaction, and lower health care costs. The Quanta SC+ is a contemporary, portable HD device intended to be operated by a broad range of lay users. It was developed in collaboration with experienced home HD patients and human factors engineers with intuitive linear workflows, on-screen step-by-step instructions, and troubleshooting help screens that are easy to navigate. This study is a descriptive report on the home HD training time of first-time users of the Quanta SC+ HD device in the United Kingdom. Methods: From August 2020 until March 2022, patients on dialysis across 5 sites in the United Kingdom were trained on the Quanta SC+ HD device as part of standard of care for self-care home HD as treatment for their kidney failure. We collected data on the number of total training weeks and sessions to be signed off as safe by a nephrologist and the frequency of training loss. Training time included organizational delays plus needling training time. Results: As of March 2022, a total of 34 patients completed training on the Quanta SC+ HD device. The mean age of the patients was 52.3 ± 15.1 years, 14 (41.2%) were female. Patients had an average dialysis vintage of 2.6 ± 2.2 years. A total of 9 (26.5%) patients were already on home HD and converted to Quanta SC+ from another device. Training time for these individuals before being signed off as safe by a nephrologist ranged from 2 to 3 weeks (6 to 9 sessions). A total of 25 (73.5%) patients converted to Quanta SC+ from another dialysis modality. The average training time for these individuals before being signed off as safe by a nephrologist was 6 weeks (18 sessions). Conclusions: A descriptive report of 34 patients with kidney failure who trained for frequent self-care home HD with the Quanta SC+ HD device reported an average training time within expectations set from national kidney organizations with minimal training. These results indicate that the device is user-friendly and intuitive to learn. Funding: Commercial Support - Quanta Dialysis Technologies
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".