Expanding Utilization of Home Dialysis: An Action Agenda From the First International Home Dialysis Roundtable
Bibliographic record
Abstract
In a groundbreaking meeting, leading global kidney disease organizations came together in the fall of 2020 as an International Home Dialysis Roundtable (IHDR) to address strategies to increase access to and uptake of home dialysis, both peritoneal dialysis and home hemodialysis. This challenge has become urgent in the wake of the coronavirus disease 2019 (COVID-19) pandemic, during which patients with advanced kidney disease, who are more susceptible to viral infections and severe complications, must be able to safely physically distance at home. To boost access to home dialysis on a global scale, IHDR members committed to collaborate, through the COVID-19 public health emergency and beyond, to promote uptake of home dialysis on a broad scale. Their commitments included increasing the reach and influence of key stakeholders with policy makers, building a cooperative of advocates and champions for home dialysis, working together to increase patient engagement and empowerment, and sharing intelligence about policy, education, and other programs so that such efforts can be operationalized globally. In the spirit of international cooperation, IHDR members agreed to document, amplify, and replicate established efforts shown to improve access to home dialysis and support new policies that facilitate access through procedures, innovation, and reimbursement.
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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 | 0.033 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.039 | 0.036 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".