Evaluating Home Dialysis Training Requirements: A Survey of Nephrology Program Directors and Division Chiefs
Bibliographic record
Abstract
Background: The American Society of Nephrology (ASN) convened a Home Dialysis Task Force in 2021 to improve awareness and outcomes of home dialysis. An identified need was to ensure universal and adequate training, education, and exposure to home dialysis during nephrology fellowship. As a first step, The Task Force surveyed program directors and division chiefs to explore perspectives on 1) what constitutes adequate home dialysis training, and 2) what home dialysis training resources are needed. Methods: Using REDCap, we anonymously surveyed program directors and division chiefs of US adult nephrology fellowship programs from 03/04/22-04/05/22. Program directors were asked to 1) select the minimum training fellows should receive before they could provide home dialysis without supervision (defined by number of clinics attended or patients seen) and 2) select home dialysis training resources that ASN could support. Division chiefs were asked to select the minimum training fellows should receive before they could be hired as faculty to manage home dialysis patients. Results: Among 158 program directors and 170 division chiefs in ASN's database, 43 and 31 responsed (response rate, 27% and 18%, respectively). When asked about the minimum training fellows should receive before they could provide peritoneal dialysis without supervision, the most common answers were 10-12 clinics (53% of program directors and 35% of division chiefs), and 11-15 patients (33% of program directors and 29% of division chiefs). For home hemodialysis training, please see Table 1. When program directors were asked which resources they would like ASN to faciliate, 74% requested a virtual case-based home dialysis mentorship program. Conclusions: Most program directors and division chiefs felt that fellows could provide home dialysis independently if they attended a minimum of 10-12 home dialysis clinics. Most program directors wanted ASN to help create a virtual case-based home dialysis mentorship program. Funding: Other NIH Support - Agency for Healthcare Research and Quality
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".