Ethical and legal perspective of implantable cardioverter defibrillator deactivation or implantable cardioverter defibrillator generator replacement in the elderly
Bibliographic record
Abstract
PURPOSE OF REVIEW: Implantable cardioverter defibrillator (ICD) implantation has become a common and standard treatment for primary and secondary prevention of sudden cardiac death in patients with poor left ventricular ejection fraction across the world. Circumstances, of course, change after the initial implant as patients age. This raises legal and ethical questions about deactivating or not replacing ICD generators when the likelihood of meaningful benefit has diminished. RECENT FINDINGS: Health professionals are reluctant to discuss the end-of-life planning with patients who have ICDs. Older patients are more likely to have multiple comorbidities that worsen or accumulate further after initial implantation and attenuate the survival benefit of ICDs. Joint guidelines suggest physicians educate patients during the initial consent process about the possibility of deactivating ICDs after implantation if their individual situation changes to the point of futility. SUMMARY: ICD deactivation and nonreplacement are unavoidable issues that require clarity for meaningful and ethical implementation. This is an ongoing process.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".