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Record W4306641108 · doi:10.14503/thij-20-7518

Does Pacemaker Implantation After Surgical Aortic Valve Replacement Impact Long-Term Morbidity and Mortality? A Focused Review

2022· review· en· W4306641108 on OpenAlexaff
Mohammad El‐Diasty, Mark Davies, Ángel L. Fernández, Igo Ribeiro, Darrin Payne, Dimitri Petsikas

Bibliographic record

VenueTexas Heart Institute Journal · 2022
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsMedicinePerioperativeAortic valve replacementComorbidityComplicationIncidence (geometry)Permanent pacemakerSurgeryRisk factorCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Permanent pacing remains a serious complication that can occur in the postoperative period of surgical aortic valve replacement. The reported incidence is variable, and there are many perioperative factors that have been linked with a greater need for permanent pacing. Permanent pacing can also be associated with late lead-related and cardiac complications that can affect late outcome. However, the degree of late dependence on pacemakers is varied, and some studies have shown that a substantial proportion of patients do not need long-term pacing. Some groups have found that permanent pacing was associated with a negative impact on long-term survival in these patients. A common finding among these studies is that the groups of patients with pacemakers had higher preoperative surgical risk and comorbidity status. This makes it difficult to establish whether permanent pacing on its own represents a risk factor for late mortality or whether it is simply a marker that reflects the higher complexity and comorbidities in this group of patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.063
GPT teacher head0.437
Teacher spread0.374 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2022
Admission routes1
Has abstractyes

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