The cirrhosis care Alberta (CCAB) protocol: implementing an evidence-based best practice order set for the management of liver cirrhosis - a hybrid type I effectiveness-implementation trial
Bibliographic record
Abstract
BACKGROUND: Liver cirrhosis is a leading cause of morbidity, premature mortality and acute care utilization in patients with digestive disease. In the province of Alberta, hospital readmission rates for patients with cirrhosis are estimated at 44% at 90 days. For hospitalized patients, multiple care gaps exist, the most notable stemming from i) the lack of a structured approach to best practice care for cirrhosis complications, ii) the lack of a structured approach to broader health needs and iii) suboptimal preparation for transition of care into the community. Cirrhosis Care Alberta (CCAB) is a 4-year multi-component pragmatic trial which aims to address these gaps. The proposed intervention is initiated at the time of hospitalization through implementation of a clinical information system embedded electronic order set for delivering evidence-based best practices under real-world conditions. The overarching objective of the CCAB trial is to demonstrate effectiveness and implementation feasibility for use of the order set in routine patient care within eight hospital sites in Alberta. METHODS: A mixed methods hybrid type I effectiveness-implementation design will be used to evaluate the effectiveness of the order set intervention. The primary outcome is a reduction in 90-day cumulative length of stay. Implementation outcomes such as reach, adoption, fidelity and maintenance will also be evaluated alongside other patient and service outcomes such as readmission rates, quality of care and cost-effectiveness. This theory-based trial will be guided by Normalization Process Theory, Consolidated Framework on Implementation Research (CFIR) and the Reach-Effectiveness-Adoption-Implementation-Maintenance (RE-AIM) Framework. DISCUSSION: The CCAB project is unique in its breadth, both in the comprehensiveness of the multi-component order set and also for the breadth of its roll-out. Lessons learned will ultimately inform the feasibility and effectiveness of this approach in "real-world" conditions as well as adoption and adaptation of these best practices within the rest of Alberta, other provinces in Canada, and beyond. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04149223, November 4, 2019.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".