MétaCan
Menu
Back to cohort
Record W2096764438 · doi:10.4021//jmc.v2i1.92

Large Mediastinal Mass in Pregnancy: Utility of Echocardiography and Cardiac MRI

2011· article· en· W2096764438 on OpenAlexvenueno aff
Giuseppina Novo, Antonino Rotolo, Salvatore Montalto, Giuseppe Coppola, M Farinella, Gianfranco Ciaramitaro, Emanuele Grassedonia, Fabrizia Centineo, Giovanni Ruvolo, Salvatore Novo, Pasquale Assennato

Bibliographic record

VenueJournal of Medical Cases · 2011
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVentricleRadiologyPericardial effusionMediastinal massEchogenicityMediastinumEpicardial fatChest painPleural effusionPregnancyMagnetic resonance imagingCardiologyInternal medicineUltrasound

Abstract

fetched live from OpenAlex

A 29-year-old woman at the 27th week of gestation was admitted to the hospital for dyspnea, chest pain radiating to the left shoulder and fatigue. An echocardiogram showed the presence of a pericardial effusion and an inhomogeneously echogenic mass, before the right ventricle, in the mediastinum, that modified the geometry of the right ventricle without impairing its filling. A cardiac MRI, subsequently performed, better defined the mass dimension and its contiguity relationship; moreover, it confirmed the suspicion of a lymphoproliferative disease. A mediastinal biopsy showed an infiltrating non-Hodgkin’s lymphoma at B big cells. After waiting until the 30th week of gestation, we proceeded to caesarean delivery, excellently succeeded, and contemporaneously to mass excision. Echocardiography was able to identify the presence of the mediastinal mass and to monitor its consequences on cardiac haemodynamic. Cardiac magnetic resonance was performed safely after the third trimester without producing apparent problems in the newborn. It helped in giving information on the mass dimension and its contiguity relationship but didn’t characterize the mass. doi:10.4021/jmc92w

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)0.001
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 score0.012
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.308
Teacher spread0.266 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Medical CasesSame topicCancer Risks and FactorsFrench-language works237,207