Rationale for a home dialysis virtual ward: design and implementation
Bibliographic record
Abstract
BACKGROUND: Home-based renal replacement therapy (RRT) [peritoneal dialysis (PD) and home hemodialysis (HHD)] offers independent quality of life and clinical advantages compared to conventional in-center hemodialysis. However, follow-up may be less complete for home dialysis patients following a change in care settings such as post hospitalization. We aim to implement a Home Dialysis Virtual Ward (HDVW) strategy, which is targeted to minimize gaps of care. METHODS/DESIGN: The HDVW Pilot Study will enroll consecutive PD and HHD patients who fulfilled any one of our inclusion criteria: 1. following discharge from hospital, 2. after interventional procedure(s), 3. prescription of anti-microbial agents, or 4. following completion of home dialysis training. Clinician-led telephone interviews are performed weekly for 2 weeks until VW discharge. Case-mix (modified Charlson Comorbidity Index), symptoms (the modified Edmonton Symptom Assessment Scale) and patient satisfaction are assessed serially. The number of VW interventions relating to eight pre-specified domains will be measured. Adverse events such as re-hospitalization and health-services utilization will be ascertained through telephone follow-up after discharge from the VW at 2, 4, 12 weeks. The VW re-hospitalization rate will be compared with a contemporary cohort (matched for age, gender, renal replacement therapy and co-morbidities). Our protocol has been approved by research ethics board (UHN: 12-5397-AE). Written informed consent for participation in the study will be obtained from participants. DISCUSSION: This report serves as a blueprint for the design and implementation of a novel health service delivery model for home dialysis patients. The major goal of the HDVW initiative is to provide appropriate and effective supports to medically complex patients in a targeted window of vulnerability. TRIAL REGISTRATION: (NCT01912001).
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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 | 0.083 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".