Predictors of Care Gaps in Home Dialysis: The Home Dialysis Virtual Ward Study
Bibliographic record
Abstract
BACKGROUND: Home dialysis patients may be at an increased risk of adverse events after transitional states. The home dialysis virtual ward (HDVW) trial was conducted in Canadian dialysis centers and aimed to evaluate potential care gaps and patient satisfaction during the HDVW. METHODS: The HDVW was a multicenter single-arm trial including peritoneal dialysis and home hemodialysis patients after 4 different events (hospital discharge, medical procedure, antibiotics, completion of training). Telephone-led interviews using a standardized assessment tool were performed over a 2-week period to assess a patient's care and adjust treatment as required. Upon completion, patients were surveyed to evaluate their perceived impact on domains of care using a rating scale; 1 not satisfied to 10 completely satisfied. RESULTS: The HDVW trial included 193 patients with a median number of potential care gaps/interventions of 1 (0-2) per patient. Patients admitted to the HDVW after hospital discharge were at a higher risk of potential gaps in care (OR 2.16, 95% CI 1.29-3.62), while longer dialysis vintage was -associated with a lower number of gaps/interventions (OR 0.97 per year, 95% CI 0.95-0.98). A total of 105/193 (54%) patients completed satisfaction surveys. Patients were highly satisfied with the HDVW (median rating scale score 8, IQR 2) and felt it had a positive impact (rating scale score ≥7) on their overall health, understanding of treatment and access to a nephrologist. CONCLUSION: The HDVW was effective at identifying several potential care gaps, and patients were satisfied across several domains of care. This intervention may be valuable in supporting home dialysis patients during care transitions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".