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Record W4391906485 · doi:10.1016/j.jacadv.2024.100853

Effect of Sex and Flow Status on Outcomes After Surgical or Transcatheter Aortic Valve Replacement

2024· article· en· W4391906485 on OpenAlexaff
Rasmus Carter‐Storch, Rebecca T. Hahn, Amr E. Abbas, Melissa A. Daubert, Pamela S. Douglas, Sammy Elmariah, Yanglu Zhao, Michael J. Mack, Martin B. Leon, Philippe Pîbarot, Marie‐Annick Clavel

Bibliographic record

VenueJACC Advances · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersEdwards Lifesciences
KeywordsMedicineAortic valve replacementInternal medicineCardiologySurgeryStenosis

Abstract

fetched live from OpenAlex

Background: despite preserved ejection fraction (paradoxical low flow [PLF]) is associated with adverse outcomes in patients with aortic stenosis undergoing transcatheter aortic valve replacement (TAVR) or surgical aortic valve replacement (SAVR). However, whether the risk associated with PLF is similar in both sexes is unknown. Objectives: The purpose of this study was to analyze the risk associated with PLF in severe aortic stenosis for men and women randomized to TAVR or SAVR. Methods: Patients with ejection fraction ≥50% from the PARTNER (Placement of Aortic Transcatheter Valves) 2 and 3 trials were stratified by sex and treatment arm. The impact of PLF on the 2-year occurrence of the composite of death or heart failure hospitalization (primary endpoint) and of all-cause mortality alone (secondary endpoint) was analyzed. Analysis of variance was used to assess baseline differences between groups. Multivariate Cox regression analysis was used to identify predictors of the endpoint. Results: > 0.05). Conclusions: In women with PLF, TAVR may improve outcomes compared to SAVR. PLF appears to have less impact on outcomes in men.

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 score0.231
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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 head0.007
GPT teacher head0.354
Teacher spread0.347 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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