Video-Based Telemedicine for Kidney Disease Care
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Video-based telemedicine provides an alternative health care delivery model for patients with CKD. The objective was to provide an overview of the available evidence on the implementation and outcomes of adopting video-based telemedicine in nephrology. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and CINAHL were searched in December 2019 and again in January 2021 for studies using video-based telemedicine for adults across the spectrum of kidney disease. Study types included peer-reviewed clinical trials, observational studies, and descriptive studies available in full text. Search results were independently screened by two authors, who then independently reviewed and extracted data from the eligible studies. Results were synthesized in tabular format, summarizing study characteristics by area within nephrology; the video-based interventions used; and clinical, health care utilization, and patient-reported outcomes. RESULTS: After reviewing 1870 unique citations, 24 studies were included (four randomized controlled trials, six cohort studies, five pre-post intervention studies, seven case series, and two qualitative studies). Video-based technology was used to facilitate care across all stages of CKD. Although earlier studies used a range of institution-specific technologies that linked main hospital sites to more remote health care locations, more recent studies used technology platforms that allowed patients to receive care in a location of their choice. Video-based care was well received, with the studies reporting high patient satisfaction and acceptable clinical outcomes. CONCLUSIONS: Video-based telemedicine is being used for kidney care and has evolved to be less reliant on specialized telemedicine equipment. As its use continues to grow, further primary studies and systematic reviews of outcomes associated with the latest innovations to video-based care in nephrology can address knowledge gaps, such as approaches to sustainable integration and minimization of barriers to access.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".