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 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".