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Long‐Term Structural Failure of Coaxial Polyurethane Implantable Cardioverter Defibrillator Leads

2002· article· en· W2017219578 on OpenAlexaff
Robert G. Hauser, David S. Cannom, David L. Hayes, Victor Parsonnet, John Hayes, Norman B. Ratliff, G. Frank O. Tyers, Andrew E. Epstein, Stephen C. Vlay, Seymour Furman, Jay Gross

Bibliographic record

VenuePacing and Clinical Electrophysiology · 2002
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineImplantable cardioverter-defibrillatorStructural failureLead (geology)Heart failureCoaxialImplantInternal medicineCardiologySurgeryStructural engineering

Abstract

fetched live from OpenAlex

Transvene models 6936/6966, a coaxial polyurethane ICD lead, may be prone to structural failure. These models comprise 54% of ICD lead failures in the authors' Multicenter Registry database. Because ICD leads perform a vital function, the clinical features, causes, and probability of Transvene 6936/6966 lead failure were determined. The Registry and United States Food and Drug Administration databases were queried for the clinical features and structural causes of the Transvene 6936/6966 lead failure, and a five-center substudy estimated the survival probability for 521 Transvene 6936/6966 implants. The mean time to failure was 4.8 +/- 2.1 years, and the estimated survival at 60 and 84 months after implant were 92% and 84%, respectively. Oversensing was the most common sign of failure (76%), and 24 patients experienced inappropriate shocks. The manufacturer's reports indicated that high voltage coil fracture and 80A polyurethane defects were the predominant causes of lead failure. Transvene models 6936 and 6966 coaxial polyurethane ICD leads are prone to failure over time. Patients who have these leads should be evaluated frequently. Additional studies are needed to identify safe management strategies.

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 score0.247
Threshold uncertainty score0.622

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 head0.024
GPT teacher head0.316
Teacher spread0.292 · 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

Citations61
Published2002
Admission routes1
Has abstractyes

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