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Record W4392900680 · doi:10.1016/j.jacadv.2024.100901

Recommendations for the Management of High-Risk Cardiac Delivery

2024· review· en· W4392900680 on OpenAlexaff
Emily S. Lau, Niti R. Aggarwal, Joan Briller, Daniela R. Crousillat, Katherine E. Economy, Colleen Harrington, Kathryn J. Lindley, Isabelle Malhamé, Deirdre Mattina, Marie‐Louise Meng, Selma F. Mohammed, Odayme Quesada, Nandita S. Scott

Bibliographic record

VenueJACC Advances · 2024
Typereview
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsMcGill University Health Centre
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineMultidisciplinary approachIntensive care medicinePregnancyMedical emergencyRisk managementDiseaseBusiness

Abstract

fetched live from OpenAlex

Maternal mortality is a major public health crisis in the United States. Cardiovascular disease (CVD) is a leading cause of maternal mortality and morbidity. Labor and delivery is a vulnerable time for pregnant individuals with CVD but there is significant heterogeneity in the management of labor and delivery in high-risk patients due in part to paucity of high-quality randomized data. The authors have convened a multidisciplinary panel of cardio-obstetrics experts including cardiologists, obstetricians and maternal fetal medicine physicians, critical care physicians, and anesthesiologists to provide a practical approach to the management of labor and delivery in high-risk individuals with CVD. This expert panel will review key elements of management from mode, timing, and location of delivery to use of invasive monitoring, cardiac devices, and mechanical circulatory support.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.008

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 head0.042
GPT teacher head0.371
Teacher spread0.328 · 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 designNot applicable
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

Citations15
Published2024
Admission routes1
Has abstractyes

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