Decision Support for Implantable Cardioverter-Defibrillator Replacement
Bibliographic record
Abstract
BACKGROUND: Decision support can help patients facing implantable cardioverter-defibrillator (ICD) replacement understand their options and reach an informed decision reflective of their preferences. OBJECTIVE: The aim of this study was to evaluate the feasibility of a decision support intervention for patients faced with the decision to replace their ICD. METHODS: A pilot feasibility randomized trial was conducted. Patients approaching ICD battery depletion were randomized to decision support intervention or usual care. Feasibility outcomes included recruitment rates, intervention use, and completeness of data; secondary outcomes were knowledge, values-choice concordance, decisional conflict, involvement in decision making, and choice. RESULTS: A total of 30 patients were randomized to intervention (n = 15) or usual care (n = 15). The intervention was used as intended, with 2% missing data. Patients in the intervention arm had better knowledge (77.4% vs 51.1%; P = .002). By 12 months, 8 of 13 (61.5%) in the intervention arm and 10 of 14 (71.4%) in the usual care arm accepted ICD replacement; 1 per arm declined (7.7% vs 7.1%, respectively). CONCLUSION: It was feasible to deliver the intervention, collect data, despite slow recruitment. The decision support intervention has the potential to improve ICD replacement decision quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".