The use of virtual physician mentoring to enhance home dialysis knowledge and uptake
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Home dialysis therapies are flexible kidney replacement strategies with documented clinical benefits. While the incidence of end-stage kidney disease continues to increase globally, the use of home dialysis remains low in most developed countries. Multiple barriers to providing home dialysis have been noted in the published literature. Among known challenges, gaps in clinician knowledge are potentially addressable with a focused education strategy. Recent national surveys in the United States and Australia have highlighted the need for enhanced home dialysis knowledge especially among nephrologists who have recently completed training. Traditional in-person continuing professional educational programmes have had modest success in promoting home dialysis and are limited by scale and the present global COVID-19 pandemic. We hypothesize that the use of a 'Hub and Spoke' model of virtual home dialysis mentorship for nephrologists based on project ECHO would support home dialysis growth. We review the home dialysis literature, known educational gaps and plausible educational interventions to address current limitations in physician education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it