SP46 Patients' Decisions about Receiving an ICD for Primary Prevention of Sudden Cardiac Death - Information Gathering in Patient Waiting Rooms from “Armchair Expert” ICD Veterans
Bibliographic record
Abstract
Purpose: Implantable defibrillators (ICD) are proven to be highly effective at reducing the risk of sudden cardiac death from arrhythmia in those deemed high risk. The overall aim of this study is to explore the process and the factors that influence patients' decisions to accept or decline an ICD for primary prevention (PP). Findings of this study will provide evidence regarding the decisions patients make when offered an ICD for PP. Methods: This ongoing work is a multi-site study based on grounded theory. The study aims to enroll 30 participants who decline and 30 who have accepted and received an ICD for PP. Participants from three academic teaching centres in Canada will be included. Criteria for participant selection include: age > 18 years; ability to provide informed consent; speak and read English; offered an ICD for PP/prophylaxis; 5) agree to individual taped interview. Patients receiving bi-ventricular ICDs will not be included. For those who have received an ICD and informed consent obtained, study interviews are arranged two weeks after implant. These occur after ICD implant so as not to influence participants' decisions. Patients who decline the ICD and consent to participate are interviewed within a month. Semi-structured interviews are audio-taped and transcribed verbatim. Initial codes have been identified by consensus between two study investigators.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".