Knowledge Translation Interventions to Improve the Timing of Dialysis Initiation
Bibliographic record
Abstract
BACKGROUND: Early initiation of chronic dialysis (starting dialysis with higher vs lower kidney function) has risen rapidly in the past 2 decades in Canada and internationally, despite absence of established health benefits and higher costs. In 2014, a Canadian guideline on the timing of dialysis initiation, recommending an intent-to-defer approach, was published. OBJECTIVE: The objective of this study is to evaluate the efficacy and safety of a knowledge translation intervention to promote the intent-to-defer approach in clinical practice. DESIGN: This study is a multicenter, 2-arm parallel, cluster randomized trial. SETTING: The study involves 55 advanced chronic kidney disease clinics across Canada. PATIENTS: Patients older than 18 years who are managed by nephrologists for more than 3 months, and initiate dialysis in the follow-up period are included in the study. MEASUREMENTS: and start dialysis in hospital as inpatients or in an emergency room setting. Secondary outcomes include the rate of change in early dialysis starts; rates of hospitalizations, deaths, and cost of predialysis care (wherever available); quarterly proportion of new starts; and acceptability of the knowledge translation materials. METHODS: We randomized 55 multidisciplinary chronic disease clinics (clusters) in Canada to receive either an active knowledge translation intervention or no intervention for the uptake of the guideline on the timing of dialysis initiation. The active knowledge translation intervention consists of audit and feedback as well as patient- and provider-directed educational tools delivered at a comprehensive in-person medical detailing visit. Control clinics are only exposed to guideline release without active dissemination. We hypothesize that the clinics randomized to the intervention group will have a lower proportion of early dialysis starts. LIMITATIONS: Limitations include passive dissemination of the guideline through publication, and lead-time and survivor bias, which favors delayed dialysis initiation. CONCLUSIONS: If successful, this active knowledge translation intervention will reduce early dialysis starts, lead to health and economic benefits, and provide a successful framework for evaluating and disseminating future guidelines. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02183987.
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".