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.
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 | 0.054 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".