Incorporating patients’ preference diagnosis in implantable cardioverter defibrillator decision-making
Bibliographic record
Abstract
PURPOSE OF REVIEW: Strong recommendations exist for implantable cardioverter defibrillators (ICD) in appropriately selected patients. Yet, patient preferences are not often incorporated when decisions about ICD therapy are made. Literature published since 2016 was reviewed aiming to discuss current advances and ongoing challenges with ICD decision-making in adults, discuss shared decision-making (SDM) as a strategy to incorporate preference diagnoses, summarize current evidence on effective interventions to facilitate SDM, and identify opportunities for research and practice. RECENT FINDINGS: Advances in risk stratification can identify patients who will most and least likely benefit from the ICD. Interventions to support SDM are emerging. These interventions present options, the risks, and the benefits of each option, and elicit patients' values and preferences regarding possible outcomes. SUMMARY: Appropriate patient selection for initial or continued ICD therapy is multifactorial. It requires accurate clinical diagnosis using careful risk stratification and accurate preference diagnosis based upon the patient's preferences. SDM aims to unite the elements that constitute these two equally important diagnoses. High-quality decision-making will be difficult to achieve if patients lack or misunderstand information, and if evolving patient preferences are not incorporated when making decisions.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".