The use of virtual physician mentoring to enhance home dialysis knowledge and uptake
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.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".