Patients’ decision making to accept or decline an implantable cardioverter defibrillator for primary prevention of sudden cardiac death
Bibliographic record
Abstract
BACKGROUND: Patients are offered implantable defibrillators (ICDs) for the prevention of sudden cardiac death (SCD). However, patients' decision-making process (DMP) of whether or not to accept an ICD has not been explored. We asked patients about their decision making when offered an ICD. DESIGN/SETTING: A grounded theory methodology was employed. Patients were recruited from three ICD centres. Those who received an ICD underwent interviews the first month after implant. Declining patients had interviews at their convenience. In-depth analysis of transcripts was completed. Identified themes were placed along process pathways in a DMP model and tested. FINDINGS: Forty-four patients consented to participate (25% women). Thirty-four accepted an ICD and 10 (23%) declined. Ages ranged from 26 to 87 (mean = 65; SD = 12.5). Participants were retired (65%), had ischaemic heart disease (64%) and some post-secondary education (52%). The DMP was triggered when patient's risk for SCD was communicated. The physician's recommendation and a new awareness SCD risk were motivators to accept the ICD. Patient's decision-making approaches fell along a continuum, from active and engaged to passive and indifferent. Patient's approaches were influenced most by the following: (i) trust; (ii) social influences and (iii) health state. CONCLUSIONS: Health-care providers need to recognize the DMP pathways in which ICD candidacy and SCD risk are understood. The factors that influence a patient's decision warrant discussion pre-implant. It is imperative that patients comprehend the meaning of ICD candidacy to make an informed decision. Participants did not recall alternatives to receiving ICD therapy.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".