Variations in provider practices in remote patient monitoring on peritoneal dialysis in the USA and Canada
Bibliographic record
Abstract
Automation has allowed clinicians to program PD treatment parameters, all while obtaining extensive individual treatment data. This data populates in a centralized online platform shortly after PD treatment completion. Individual treatment data available to providers includes patients' vital signs, alarms, bypasses, prescribed PD treatment, actual treatment length, individual cycle fill volumes, ultrafiltration volumes, as well as fill, dwell, and drain times. However, there is no guidance about how often or if this data should be assessed by the clinical team members. We set out to determine current practice patterns by surveying members of the home dialysis team managing PD patients across the United States and Canada. A total of 127 providers completed the survey. While 91% of respondents reported having access to a remote monitoring platform, only 31% reported having a standardized protocol for data monitoring. Rating their perceived importance of having a standard protocol for remote data monitoring, on a scale of 0 (not important at all) to 10 (extremely important), the average response was 8 (physicians 7; nurses 9). Most nurses reported reviewing the data multiple times per week, whereas most physicians reported viewing the data only during regular/monthly visits. Although most of the providers who responded have access to remote monitoring data and feel that regular review is important, the degree of its utilization is variable, and the way in which the information is used is not commonly protocolized. Working to standardize data interpretation, testing algorithms, and educating providers to help process and present the data are important next steps.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".