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Record W1986645924 · doi:10.1093/europace/euq313

Characteristics of patients and implantable defibrillators associated with failure to sense device alert systems

2010· article· en· W1986645924 on OpenAlexaff
M. Bennett, Charles R. Kerr, E. Hahn, S. Flavelle, C. McIlroy, Stanley Tung

Bibliographic record

VenueEP Europace · 2010
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineImplantable cardioverter-defibrillatorMedical emergencyALARMEmergency medicineCardiology

Abstract

fetched live from OpenAlex

AIMS: In the era of increasing implantable cardioverter defibrillator (ICD) complexity, the ICD patient alert is deemed to be an important feature in the early detection of ICD system malfunction and is either an audible or a vibratory alert. We sought to evaluate the patient's ability to detect these ICD alerts in the device clinic setting as a surrogate endpoint of clinical utility. METHODS AND RESULTS: From 1 November 2006 to 31 March 2008, 563 patients with an ICD equipped with either an audible patient alert (APA, Medtronic and Guidant; n = 485) or a vibratory monitoring alert ([VMA, St Jude Medical; n = 78) had their alarm demonstrated in the quiet clinic setting. The ability to recognize the alert was analysed and then stratified by gender, age, manufacturer, type of alert, and pocket location. The average patient age was 63.3 (± 13.6) years and 82.8% of patients were male. Implantable cardioverter defibrillator manufacturers were Medtronic (n = 464), Boston Scientific (n = 21), and SJM (n = 78). The APA was heard in 86.0% of patients. This was less likely in patients who were older, male, and where the device was placed in the submuscular position. Every patient with a VMA sensed their alert. CONCLUSION: In the current ICD alert technology, the ability to sense the ICD alert in the device clinic appears to be higher for the VMA than for the APA. In particular, older patients and male patients are less likely to sense the APA.

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 score0.039
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.231
Teacher spread0.223 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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