Home dialysis: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
Home dialysis modalities (home hemodialysis [HD] and peritoneal dialysis [PD]) are associated with greater patient autonomy and treatment satisfaction compared with in-center modalities, yet the level of home-dialysis use worldwide is low. Reasons for limited utilization are context-dependent, informed by local resources, dialysis costs, access to healthcare, health system policies, provider bias or preferences, cultural beliefs, individual lifestyle concerns, potential care-partner time, and financial burdens. In May 2021, KDIGO (Kidney Disease: Improving Global Outcomes) convened a controversies conference on home dialysis, focusing on how modality choice and distribution are determined and strategies to expand home-dialysis use. Participants recognized that expanding use of home dialysis within a given health system requires alignment of policy, fiscal resources, organizational structure, provider incentives, and accountability. Clinical outcomes across all dialysis modalities are largely similar, but for specific clinical measures, one modality may have advantages over another. Therefore, choice among available modalities is preference-sensitive, with consideration of quality of life, life goals, clinical characteristics, family or care-partner support, and living environment. Ideally, individuals, their care-partners, and their healthcare teams will employ shared decision-making in assessing initial and subsequent kidney failure treatment options. To meet this goal, iterative, high-quality education and support for healthcare professionals, patients, and care-partners are priorities. Everyone who faces dialysis should have access to home therapy. Facilitating universal access to home dialysis and expanding utilization requires alignment of policy considerations and resources at the dialysis-center level, with clear leadership from informed and motivated clinical teams.
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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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.009 | 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".