Systems Innovations to Increase Home Dialysis Utilization
Bibliographic record
Abstract
Globally, there is an interest to increase home dialysis utilization. The most recent United States Renal Data System (USRDS) data report that 13.3% of incident dialysis patients in the United States are started on home dialysis, while most patients continue to initiate KRT with in-center hemodialysis. To effect meaningful change, a multifaceted innovative approach will be needed to substantially increase the use of home dialysis. Patient and provider education is the first step to enhance home dialysis knowledge awareness. Ideally, one should maximize the number of patients with CKD stage 5 transitioning to home therapies. If this is not possible, infrastructures including transitional dialysis units and community dialysis houses may help patients increase self-care efficacy and eventually transition care to home. From a policy perspective, adopting a home dialysis preference mandate and providing financial support to recuperate increased costs for patients and providers have led to higher uptake in home dialysis. Finally, respite care and planned home-to-home transitions can reduce the incidence of transitioning to in-center hemodialysis. We speculate that an ecosystem of complementary system innovations is needed to cause a sufficient change in patient and provider behavior, which will ultimately modify overall home dialysis utilization.
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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 | 0.004 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".