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Record W2329717739 · doi:10.1097/hco.0b013e32835b0b3b

Ethical and legal perspective of implantable cardioverter defibrillator deactivation or implantable cardioverter defibrillator generator replacement in the elderly

2012· review· en· W2329717739 on OpenAlexaff
Gary A. Wright, George J. Klein, Lorne J. Gula

Bibliographic record

VenueCurrent Opinion in Cardiology · 2012
Typereview
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineImplantable cardioverter-defibrillatorSudden cardiac deathEjection fractionCLARITYPrimary preventionSecondary preventionIntensive care medicineCardiac resynchronization therapyInternal medicineCardiologyHeart failure

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.132
GPT teacher head0.418
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations29
Published2012
Admission routes1
Has abstractyes

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