MétaCan
Menu
Back to cohort
Record W2169539116 · doi:10.1177/082585971102700103

Patients’ Perspectives on End-Of-Life Issues and Implantable Cardioverter Defibrillators

2011· article· en· W2169539116 on OpenAlexafffundabout
Patricia H. Strachan, Sandra Carroll, Sonya de Laat, Lisa Schwartz, Heather M. Arthur

Bibliographic record

VenueJournal of Palliative Care · 2011
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsHeart and Stroke FoundationMcMaster University
FundersCanadian Institutes of Health Research
KeywordsImplantable cardioverter-defibrillatorMedicineEnd-of-life careMedical emergencyIntensive care medicinePalliative careCardiologyNursing

Abstract

fetched live from OpenAlex

Increasing numbers of cardiovascular patients are receiving implantable cardioverter defibrillators (ICDs) for primary prevention of sudden cardiac death (SCD). This report examines patients' perspectives on related end-of-life issues. Using a grounded theory approach, audiotaped, semi-structured interviews were undertaken with 30 participants from two ICD referral centres in southwestern Ontario (24 who accepted an ICD and 6 who declined). Interview transcripts, verification with interviewees, researcher memos, published literature, and participant demographics were analyzed using NVivo7. Most participants were male, had comorbidities and an ejection fraction of less than 30 percent, and ranged in age from 26 to 87. Consensus was reached by three research team members on three main themes: quality versus quantity of life, preferred mode of death, and the technical realities of the ICD. The ICD was considered in relation to both quantity and quality of life. Most participants focused on the prevention of SCD, not the implications of the ICD for death by any other cause. Participants advocated for incorporating the ICD into advance care planning. Our findings have implications for the development of advance care plans and education of health professionals.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.308
Teacher spread0.265 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations39
Published2011
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Palliative CareSame topicCardiac pacing and defibrillation studiesFrench-language works237,207