The integrated care model: facilitating initiation of or transition to home dialysis
Bibliographic record
Abstract
A proportion of end-stage kidney disease (ESKD) patients require kidney replacement therapy to maintain clinical stability. Home dialysis therapies offer convenience, autonomy and potential quality of life improvements, all of which were heightened during the COVID-19 pandemic. While the superiority of specific modalities remains uncertain, patient choice and informed decision-making remain crucial. Missed opportunities for home therapies arise from systemic, programmatic and patient-level barriers. This paper introduces the integrated care model which prioritizes the safe and effective uptake of home therapies while also emphasizing patient-centered care, informed decision-making, and comprehensive support. The integrated care framework addresses challenges in patient identification, assessment, eligibility determination, education and modality transitions. Special considerations for urgent dialysis starts are discussed, acknowledging the unique barriers faced by this population. Continuous quality improvement is emphasized, with the understanding that local challenges may require tailored solutions. Overall, the integrated care model aims to create a seamless and beneficial transition to home dialysis therapies, promoting flexibility and improved quality of life for ESKD patients globally.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".