Assessing physician knowledge regarding indications for a primary prevention implantable defibrillator and potential barriers for referral
Bibliographic record
Abstract
BACKGROUND: Although there is clear evidence to demonstrate that primary prevention implantable defibrillators (ICDs) reduce mortality in high-risk patients, ICDs are underutilized. Limited data exist assessing referring physicians' knowledge about guideline indications and attitudes towards ICDs, which may influence decision for referral. METHODS AND RESULTS: The Arrhythmia Working Group from the Alberta Cardiovascular and Stroke Strategic Clinical Network developed a web-based survey consisting of case scenarios regarding primary prevention ICD indications and a list of barriers for referral to aid in the design of a complex device care pathway. We invited referring physicians to participate in the survey including internists and cardiologists and cardiology residents. The survey was completed by 109 of 799 (response rate = 14%) of physicians. Of those, 55% were internists, 32% cardiologists, and 13% cardiology residents. The majority of physicians were male (62%), practicing in a university hospital (66%). Overall, complete guideline-concordant answers were provided by 34% of physicians. In multivariable analysis, predictors of complete guideline concordance were being a cardiologist (odd ratio [OR] 5.9, confidence interval [CI] 2.1-16.4, P = 0.001) and cardiology resident (OR 6.7, CI 1.7-27.3, P = 0.007). The most common barrier for referral for internists was lack of confidence in knowledge of guideline recommendations; while cardiologists reported concerns about cost-effectiveness and cardiology residents were most concerned with inappropriate shocks. CONCLUSION: Knowledge regarding indications for primary prevention ICD is limited and varies significantly among referring physicians. The barriers for referral differ among physician groups and addressing these identified barriers may help to improve appropriate 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".