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Record W4213315957 · doi:10.14740/jmc2437w

Type I Aortic Dissection on Pregnant Woman

2016· article· en· W4213315957 on OpenAlexvenueno aff
Şükrü Gürbüz, Irfan Bayhan, Muhammet Gökhan Turtay, Hakan Oğuztürk, Serdar Derya, İsmail Okan Yıldırım

Bibliographic record

VenueJournal of Medical Cases · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAortic dissectionDissection (medical)PregnancyFetusSurgeryAortaRadiologyAortographyAneurysmEpigastric painPostpartum periodMagnetic resonance imagingCardiology

Abstract

fetched live from OpenAlex

Acute aortic dissection is rare in young women. Half of the dissections in women younger than 40 years old occur during pregnancy, typically in third trimester and in postpartum period. The reasons for increased rate of aortic dissection in pregnancy are known to be increased sex hormones, degeneration of elastic tissue in aorta, and pressure of the uterus on aorta and distal iliac arteries causing pathological alterations in the artery walls by increasing the resistance to distal flow. Aortic dissection is a condition that can be presented with different symptoms and can be fatal for both mother and the fetus if the diagnosis is missed or late. Despite the diagnosis tools like computerized tomography, echocardiography, magnetic resonance imaging or aortography, suspicion of aortic dissection is still the basis of diagnosis. Treatment objective is to provide the security of the mother and the fetus. In this case presentation, approach to pregnancy-induced aortic dissection was discussed by presenting a case in which a 41-year-old woman in 34th week presented with swelling in the legs, pain in the epigastric region and blurry vision complaints, developed type A aortic dissection and died during the follow-up. J Med Cases. 2016;7(3):105-108 doi: http://dx.doi.org/10.14740/jmc2437w

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)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.328
Teacher spread0.302 · 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.

Study designOther design
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

Citations2
Published2016
Admission routes1
Has abstractyes

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