Virtual Care Utilization and Peritonitis Risk in Rural and Indigenous Peritoneal Dialysis Patients
Bibliographic record
Abstract
Introduction Peritoneal dialysis (PD) provides an essential home-based kidney replacement therapy, particularly for rural and Indigenous populations with limited access to in-center hemodialysis. Trust, continuity, and technical proficiency are critical for safe PD care. The COVID-19 pandemic accelerated virtual care delivery, coinciding with increased PD-associated peritonitis. Methods We conducted a retrospective cohort study (2021–2023) with an embedded qualitative component at a regional hospital in Northern British Columbia, Canada. Demographic, clinical, and virtual care data were extracted from the provincial databases and patient charts. Peritonitis episodes were classified using structured Root Cause Analysis. Multivariable logistic regression assessed associations between patient factors and peritonitis risk. Semi-structured interviews with 12 PD patients explored perceptions of trust, training adequacy, and care continuity. Results Among 45 adult PD patients (mean age 63.5 ± 12.1 years), 78 peritonitis episodes occurred. Patients from rural Indigenous communities represented 33.3% of the cohort but accounted for 70.5% of episodes. Root Cause Analysis attributed 63.8% of episodes to technique failure, 19.1% to acute events, and 17.0% to psychosocial stressors. Peritonitis was associated with residence in an Indigenous community (OR 2.45, 95% CI 1.01–5.94, p=0.049), high virtual care exposure (≥85% of visits; OR 2.85, 95% CI 1.19–6.84, p=0.019), and technique failure (OR 3.12, 95% CI 1.42–6.84, p=0.005). Qualitative themes included diminished trust, inadequate hands-on training, and perceived clinical detachment. Conclusion High virtual care exposure was associated with increased risk of peritonitis, particularly among patients in rural Indigenous communities. Adapting PD models to strengthen trust, hands-on training, and cultural safety may improve outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".