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Record W2038219829 · doi:10.1016/s1474-5151(09)60157-2

SP46 Patients' Decisions about Receiving an ICD for Primary Prevention of Sudden Cardiac Death - Information Gathering in Patient Waiting Rooms from “Armchair Expert” ICD Veterans

2009· article· en· W2038219829 on OpenAlexaboutno aff
Sandra Carroll, Patricia H. Strachan, L. Schwartz, Heather M. Arthur

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMedical emergencyICD-10Sudden cardiac deathFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

Purpose: Implantable defibrillators (ICD) are proven to be highly effective at reducing the risk of sudden cardiac death from arrhythmia in those deemed high risk. The overall aim of this study is to explore the process and the factors that influence patients' decisions to accept or decline an ICD for primary prevention (PP). Findings of this study will provide evidence regarding the decisions patients make when offered an ICD for PP. Methods: This ongoing work is a multi-site study based on grounded theory. The study aims to enroll 30 participants who decline and 30 who have accepted and received an ICD for PP. Participants from three academic teaching centres in Canada will be included. Criteria for participant selection include: age > 18 years; ability to provide informed consent; speak and read English; offered an ICD for PP/prophylaxis; 5) agree to individual taped interview. Patients receiving bi-ventricular ICDs will not be included. For those who have received an ICD and informed consent obtained, study interviews are arranged two weeks after implant. These occur after ICD implant so as not to influence participants' decisions. Patients who decline the ICD and consent to participate are interviewed within a month. Semi-structured interviews are audio-taped and transcribed verbatim. Initial codes have been identified by consensus between two study investigators.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.758

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.275
Teacher spread0.253 · 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 designOther design
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

Citations1
Published2009
Admission routes1
Has abstractyes

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