Remote Patient Management for Home Dialysis Patients
Bibliographic record
Abstract
Remote patient management (RPM) offers renal health care providers and patients with end-stage kidney disease opportunities to embrace home dialysis therapies with greater confidence and the potential to obtain better clinical outcomes. Barriers and evidence required to increase adoption of RPM by the nephrology community need to be clearly defined. Ten health care providers from specialties including nephrology, cardiology, pediatrics, epidemiology, nursing, and health informatics with experience in home dialysis and the use of RPM systems gathered in Vienna, Austria to discuss opportunities for, barriers to, and system requirements of RPM as it applies to the home dialysis patient. Although improved outcomes and cost-effectiveness of RPM have been demonstrated in patients with diabetes mellitus and heart disease, only observational data on RPM have been gathered in patients on dialysis. The current review focused on RPM systems currently in use, on how RPM should be integrated into future care, and on the evidence needed for optimized implementation to improve clinical and economic outcomes. Randomized controlled trials and/or large observational studies could inform the most effective and economical use of RPM in home dialysis. These studies are needed to establish the value of existing and/or future RPM models among patients, policy makers, and health care providers.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".