Transitional Care Units in the United States: A Model for Improving Dialysis Care
Bibliographic record
Abstract
ESKD incidence rates are increasing, and mortality rates remain high. Fewer than 15% of patients use home dialysis modalities or receive preemptive kidney transplants. To address these shortcomings, executive order 13,879 (the Advancing American Kidney Health Initiative) directs the Centers for Medicare & Medicaid Services to encourage home dialysis and increase access to kidney transplants. Transitional care units (TCUs) have the potential to promote these goals by filling the gaps in care. TCUs are outpatient dialysis units for patients initiating dialysis with little or no predialysis care that provides education, facilitates smooth transitions to home or in-center dialysis, and expedites referrals to transplant clinics. TCUs offer patient-centered education, enhanced case management, and emotional support. This can improve vascular access outcomes, home dialysis utilization, and transplant referral rates. TCUs potentially address barriers to home dialysis and ideally compensate for inadequate pre-ESKD care. We performed a narrative review of several studies concerning the effect of TCUs on home dialysis utilization and patient outcomes. Eight primary studies from the United States, Canada, and the United Kingdom were reviewed, with about 7400 patients from several health and payer systems. We focused on TCU programs representing multiple payer systems, variable cohort selection criteria, and manuscripts that addressed Centers for Medicare & Medicaid Services quality measures and/or patient-centered outcomes. We call attention to the small numbers of TCUs in the United States and suggest that expansion of TCU's could benefit patients, particularly those who start dialysis under urgent conditions, which could promote equity within the ESKD population.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".