Characteristics and Effectiveness of Dedicated Care Programs for Patients Starting Dialysis: A Systematic Review
Bibliographic record
Abstract
Background Dedicated care programs that provide increased support to patients starting dialysis are increasingly being used to reduce the risk of complications. The objectives of this systematic review were to determine the characteristics of existing programs and their effect on patient outcomes. Methods We searched Embase, MEDLINE, Web of Science, Cochrane CENTRAL, and CINAHL from database inception to November 20, 2019 for English-language studies that evaluated dedicated care programs for adults starting maintenance dialysis in the inpatient or outpatient setting. Any study design was eligible, but we required the presence of a control group and prespecified patient outcomes. We extracted data describing the nature of the interventions, their components, and the reported benefits. Results The literature search yielded 12,681 studies. We evaluated 66 full texts and included 11 studies ( n =6812 intervention patients); eight of the studies evaluated hemodialysis programs. All studies were observational, and there were no randomized controlled trials. The most common interventions included patient education ( n =11) and case management ( n =5), with nurses involved in nine programs. The most common outcomes were mortality ( n =8) and vascular access ( n =4), with only three studies reporting on the uptake of home dialysis and none on transplantation. We identified four high-quality studies that combined patient education and case management; in these programs, the relative reduction in 90-day mortality ranged from 22% (95% CI, −3% to 41%) to 49% (95% CI, 33% to 61%). Pooled analysis was not possible due to study heterogeneity. Conclusions Few studies have evaluated dedicated care programs for patients starting dialysis, especially their effect on home dialysis and transplantation. Whereas multidisciplinary care models that combine patient education with case management appear to be promising, additional prospective studies that involve patients in their design and execution are needed before widespread implementation of these resource-intensive programs.
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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 | 0.011 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".