Abstract 157: Assessing Physician Knowledge Regarding Indications for a Primary Prevention Implantable Defibrillator and Potential Barriers for Referral
Bibliographic record
Abstract
Background: Primary prevention implantable cardioverter defibrillators (ICD) are under-utilized despite multiple clinical trials that demonstrated reduced mortality and cost-effectiveness in patients at risk for sudden cardiac death. Our objectives were to determine physician knowledge about primary prevention ICD guidelines and to identify potential barriers impacting referral rates. Methods: The Cardiovascular Arrhythmia and Stroke Working Group from Alberta, Canada developed a web- based survey as part of a quality assurance initiative to aid in the design of a complex device care pathway. The survey consisted of five case scenarios regarding primary prevention ICD guidelines and a list of potential barriers for ICD referral. Through expert consensus, case scenarios were developed based on current device guidelines. The survey was administered to physicians encountering patients eligible for ICD therapy, including General Internists and Cardiologists with Alberta Medical Association membership and Cardiology residents. Results: The survey was completed by 109 of 799 (response rate =14%). Of those, 55% were General Internists, 32% were Cardiologists and 13% were Cardiology residents. The majority of physicians were male (62%) and practicing at a University Hospital (66%). Overall, 34% of participants answered all case scenarios correctly. A correct answer on all five case scenarios was demonstrated by 62.5% of Cardiologists, 61.5% of Cardiology residents and 16% of General Internists (p<0.0001). Figure 1 demonstrates significant differences regarding perceived barriers for ICD referral among physician groups (p<0.0001). There were also significant differences among physician age groups (p<0.0001), with younger physicians reporting more barriers. The most common barriers among all physician groups were cost- effectiveness (55%), concerns regarding knowledge of ICD guidelines (47%) and the risk of inappropriate shocks (41%). Conclusion: Knowledge of indications for a primary prevention ICD is poor and a recognized barrier among physicians who may refer patients for device therapy. Adequate knowledge translation of ICD guidelines is crucial in order to improve ICD utilization.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".