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Cardiac Surgery in Women in the Current Era: What Are the Gaps in Care?

2021· review· en· W3203559832 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCirculation · 2021
Typereview
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCurrent (fluid)Intensive care medicineGeneral surgerySurgeryCardiology

Abstract

fetched live from OpenAlex

Cardiovascular disease remains the leading cause of morbidity and mortality for women in United States and worldwide. One in 3 women dies from cardiovascular disease, and 45% of women >20 years old have some form of CVD. Historically, women have had higher morbidity and mortality after cardiac surgery. Sex influences pathogenesis, pathophysiology, presentation, postoperative complications, surgical outcomes, and survival. This review summarizes current cardiovascular surgery outcomes as they pertain to women. Specifically, this article seeks to address whether sex disparities in research, surgical referral, and outcomes still exist and to provide strategies to close these gaps. In addition, with the growing population of women of reproductive age with cardiovascular disease and cardiovascular risk factors, indications for cardiac surgery arise in pregnant women. The current review will also address the unique issues associated with this special population.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.332
Teacher spread0.286 · 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