Interventions to improve hemodialysis adherence: A systematic review of randomized‐controlled trials
Bibliographic record
Abstract
Over 485,000 people in the United States have chronic kidney disease, a progressive kidney disease that may lead to hemodialysis. Hemodialysis involves a complex regimen of treatment, medication, fluid, and diet management. In 2005, over 312,000 patients were undergoing hemodialysis in the United States. Dialysis nonadherence rates range from 8.5% to 86%. Dialysis therapy treatment nonadherence, including treatment, medication, fluid, and diet nonadherence, significantly increases the risk of morbidity and mortality. The purpose of this paper is to systematically review randomized-controlled trial intervention studies designed to increase treatment, medication, fluid, and diet adherence in adult hemodialysis patients. A search of Cumulative Index of Nursing and Allied Health Literature (CINAHL) (1982 to May 2008), MEDLINE (1950 to May 2008), PsycINFO (1806 to May 2008), and all Evidence-Based Medicine (EBM) Reviews (Cochran DSR, ACP Journal Club, DARE, and CCTR) was conducted to identify randomized-controlled studies that tested the efficacy of interventions to improve adherence in adult hemodialysis patients. Eight randomized-controlled trials met criteria for inclusion. Six of the 8 studies found statistically significant improvement in adherence with the intervention. Of these 6 intervention studies, all studies had a cognitive component, with 3 studies utilizing cognitive/behavioral intervention strategies. Based on this systematic review, interventions utilizing a cognitive or cognitive/behavioral component appear to show the most promise for future study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".