Implementation of a Sudden Cardiac Death Risk Prediction Tool in Clinical Practice Through Electronic Health Records (INSERT-HCM Study Design)
Bibliographic record
Abstract
Sudden cardiac death (SCD) is a leading cause of mortality in children with hypertrophic cardiomyopathy (HCM). The PRecIsion Medicine in CardiomYopathy consortium developed a validated tool (PRIMaCY) for SCD risk prediction to help with ICD shared decision-making, as recommended by Clinical Practice Guidelines. The ImplemeNting a Sudden cardiac dEath Risk assessment Tool in childhood HCM (INSERT-HCM) study aims to implement PRIMaCY into electronic health records (EHR) and assess implementation determinants and outcomes. INSERT-HCM is a prospective, multicentre, hybrid Type 3 mixed methods implementation study of an EHR-embedded risk calculator across Canadian pediatric hospitals. The Active Implementation Framework will inform a staged implementation process, with organization-based implementation teams facilitating the implementation technical installation and implementation process. PRIMaCY will be installed as a user-tested EHR-integrated tool and implemented in practice using an organization and provider-focused strategy at participating hospitals. Technical installation and implementation strategies will be optimized for each healthcare setting. The Implementation Outcomes Taxonomy will inform implementation outcomes. Back-end EHR data will assess tool adoption, penetration, and fidelity. The Consolidated Framework for Implementation Research will assess implementation determinants (facilitators and barriers), and sustainability in clinical practice will be explored. INSERT-HCM will inform best practices for implementing an evidence-based digital health solution within hospital EHRs and clinical workflows to improve guideline-directed care. Developing an effective implementation strategy will inform the future dissemination of EHR-integrated digital health tools to the broader scientific and clinical community.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".