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Record W4308023631 · doi:10.1093/ehjcr/ytac424

The multidisciplinary management of a mechanical mitral valve thrombosis in pregnancy: a case report and review of the literature

2022· article· en· W4308023631 on OpenAlexaff
Jennifer M. Wright, Natalie Bottega, Judith Therrien, Roupen Hatzakorzian, Jean Buithieu, Dominique Shum‐Tim, Karen Wou, Amale Ghandour, Patricia Pelletier, William Li Pi Shan, Ian Kaufman, Richard Brown, Isabelle Malhamé

Bibliographic record

VenueEuropean Heart Journal - Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsJewish General HospitalMcGill University Health Centre
Fundersnot available
KeywordsMedicinePregnancyCaesarean sectionThrombosisMultidisciplinary teamMechanical heartMitral valveMechanical valveComplicationCardiac surgerySurgeryMitral valve replacementGestationMultidisciplinary approachIntensive care medicine

Abstract

fetched live from OpenAlex

Background: The management of anticoagulation for mechanical heart valves during pregnancy poses a unique challenge. Mechanical valve thrombosis is a devastating complication for which surgery is often the treatment of choice. However, cardiac surgery for prosthetic valve dysfunction in pregnant patients confers a high risk of maternofetal morbidity and mortality. Case summary: A 39-year-old woman in her first pregnancy at 30 weeks gestation presented to hospital with a mechanical mitral valve thrombosis despite therapeutic anticoagulation with low-molecular-weight heparin. She underwent an emergent caesarean section followed immediately by a bioprosthetic mitral valve replacement. This occurred after careful planning and organization on the part of a large multidisciplinary team. Discussion: A proactive, rather than reactive, approach to the surgical management of a mechanical valve thrombosis in pregnancy will maximize the chances of successful maternal and fetal outcomes.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.323
Teacher spread0.294 · 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 designCase report
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

Citations4
Published2022
Admission routes1
Has abstractyes

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