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Record W4291129531 · doi:10.1016/j.jscai.2022.100434

Late Outcomes of Permanent Pacemaker Implantation After TAVR: Meta-analysis of Reconstructed Time-to-Event Data

2022· review· en· W4291129531 on OpenAlexaff
Michel Pompeu Sá, Xander Jacquemyn, Tian Sun, Jef Van den Eynde, Panagiotis Tasoudis, Ozgun Erten, Serge Sicouri, Gianluca Torregrossa, Marie‐Annick Clavel, Philippe Pîbarot, Basel Ramlawi

Bibliographic record

VenueJournal of the Society for Cardiovascular Angiography & Interventions · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersEdwards LifesciencesThoracic Surgery FoundationMedtronic
KeywordsPermanent pacemakerMeta-analysisEvent (particle physics)Event dataMedicineInternal medicineCardiologyComputer scienceData miningPhysics

Abstract

fetched live from OpenAlex

Background: Permanent pacemaker implantation (PPI) after transcatheter aortic valve replacement (TAVR) is relatively frequent, and its impact on outcomes during follow-up remains a matter of discussion. Previous meta-analyses have yielded conflicting results. Methods: To compare late outcomes in patients after TAVR with and without PPI, PubMed/MEDLINE, Embase, and Google Scholar were searched for studies that reported rates of mortality/survival, rehospitalization for heart failure (HF), stroke, and/or endocarditis accompanied by at least 1 Kaplan-Meier curve for any of these outcomes. We adopted a 2-stage approach to reconstruct individual patient data on the basis of the published Kaplan-Meier graphs. Results: = .925) during follow-up. Conclusions: Patients who undergo PPI after TAVR experience higher risk of mortality and HF-related rehospitalization over time. These findings provide support for the implementation of procedural strategies to prevent heart conduction disorder and, thus, avoid PPI at the time of TAVR.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.825
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.831
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.113
GPT teacher head0.408
Teacher spread0.295 · 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 designMeta-analysis
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

Citations46
Published2022
Admission routes1
Has abstractyes

Explore more

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