A Pragmatic Randomized Controlled Trial of a CKD-Specific Virtual Monitoring Platform to Minimize Adverse Outcomes in High-Risk CKD Patients: A Clinical Research Protocol
Bibliographic record
Abstract
Background: The transition from advanced Chronic Kidney Disease (CKD) to dialysis is a period of heightened vulnerability for many patients. Virtual monitoring of these patients could facilitate the communication of accurate and reliable data between patients and health care providers, helping to avoid unnecessary emergency department (ED) visits and facilitate more optimal dialysis starts. Objective: To determine whether the addition of the VIEWER (Virtual Ward Incorporating Electronic Wearables) platform to usual care will lead to a reduction in ED visits and hospitalizations and lead to an increase in perceived safety of virtual care among patients and providers. Design: This study is a national, pragmatic, multicenter randomized controlled trial comparing usual care alone vs usual care plus the VIEWER virtual care platform in patients with advanced CKD. Given the nature of the intervention, patients and care providers will not be blinded; outcome assessment and statistical analysis will be blinded. Setting: Five CKD clinics in 2 Canadian provinces (Manitoba and Ontario). Participants: , 2-year kidney failure risk >40%). Measurements: saturation, step count) and weekly Edmonton Symptom Assessment System Revised (ESAS-r) survey. These assessments will be integrated into clinical decision-making in multidisciplinary kidney health clinics. Participants will use the VIEWER platform for 12 months (or until dialysis initiation) in addition to receiving usual care. Methods: Intention-to-treat (ITT) approach will be used to compare the primary outcome between 2 study groups. Time to primary and secondary outcomes will be assessed using univariate Cox proportional hazards models and a Kaplan-Meier analysis with a log-rank test. The primary outcome is the time to first hospital admission and/or ED visit. The control is usual care (no exposure to VIEWER platform). Limitations: Some individuals may face challenges with technology adoption, which could affect participation. Those without Internet access are limited in their ability to take part in this study. Conclusions: This study will help determine whether virtual monitoring in advanced CKD patients can reduce ED visits and hospitalization. Trial registration: Clinicaltrials.gov; identifier: NCT05726526.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".