A study protocol for improving the delivery of acute kidney replacement therapy (KRT) to critically ill patients in Alberta – DIALYZING WISELY
Bibliographic record
Abstract
BACKGROUND: Acute kidney replacement therapy (KRT) is delivered to acutely ill patients to support organ function and life in the Intensive Care Unit (ICU). Implementing standardized acute KRT pathways can ensure its safe and effective management. At present, there is no standardized approach to the management of acute KRT in Alberta ICUs. METHODS: Dialyzing Wisely is a registry embedded, stepped-wedge, interrupted time-series evaluation of the implementation of a standardized, stakeholder-informed, and evidence-based acute KRT pathway into Alberta ICUs. The acute KRT pathway will consist of two distinct phases. First, we will implement routine monitoring of evidence-informed key performance indicators (KPIs) of acute KRT. Second, we will provide prescriber and program reports for acute KRT initiation patterns. After the implementation of both phases of the pathway, we will evaluate acute KRT performance quarterly and implement a customized suite of interventions aimed at improving performance. We will compare this with baseline and evaluate iterative post implementation effects of the care pathway. DISCUSSION: Dialyzing Wisely will implement, monitor, and report a suite of KPIs of acute KRT, coupled with a care pathway that will transform the quality of acute KRT across ICUs in Alberta. This program will provide a framework for scaling evidence-informed approaches to monitoring and management of acute KRT in other jurisdictions. We anticipate improvements in acute KRT performance, decreased healthcare system costs and improved patient quality of life by decreasing patient dependence on maintenance dialysis. TRIAL REGISTRATION: Clinicaltrials.gov , NCT05186636. Registered 11, January, 2022.
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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 | 0.060 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.102 | 0.020 |
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".