ETC Model: How One Small Dialysis Organization Is Navigating Uncharted Policy Waters
Bibliographic record
Abstract
The ETC model proposes to increase access to home dialysis and transplant for patients with ESRD. Implementation of this model is happening while many dialysis organizations are still suffering the far-reaching effects of the coronavirus disease 2019 (COVID-19) pandemic. In addition, the model has the potential to negatively affect small and independent dialysis organizations disproportionately. It incentivizes home dialysis over transplant and promotes development of new home dialysis programs, rewards achievement over improvement, and places an excessive burden on small and independent dialysis organizations. Advantages of the program include the focus on self-care as an acceptable alternative to home dialysis for some patients and the potential for some organizations to make improvements in care with increased reimbursements. The authors hope that the Centers for Medicare and Medicaid Services will address many of these concerns in updated rulemaking and guidance.
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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 | 0.039 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.034 | 0.027 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.025 | 0.020 |
| Insufficient payload (model declined to judge) | 0.016 | 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".