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Record W2331088492 · doi:10.1136/bcr-2016-215146

Right atrial mass in a patient with breast cancer: percutaneous transcatheter biopsy under intracardiac echocardiography guidance

2016· article· en· W2331088492 on OpenAlexaff
Lorenzo Azzalini, Quentin de Hemptinne, Anita Asgar, Réda Ibrahim

Bibliographic record

VenueBMJ Case Reports · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiac tumors and thrombi
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineIntracardiac injectionBiopsyRadiologyPercutaneousBreast cancerCancerThrombusSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Precise diagnosis of intracardiac masses is fundamental to their treatment. However, the findings of non-invasive imaging techniques are frequently inconclusive. In this setting, percutaneous transcatheter biopsy might represent a valid alternative to surgical biopsy. Intracardiac echocardiography (ICE)-guided biopsy offers high-quality imaging, is a relatively quick and easy interventional procedure to perform and does not require patient intubation or the assistance of an echocardiographer. We describe the case of a 47-year-old woman undergoing chemotherapy for breast cancer, who presented with a right atrial mass. Non-invasive imaging was inconclusive. Since no changes in the aspect or size of the mass were noticed after 2-week treatment with heparin, ICE-guided biopsy was performed, which confirmed the thrombotic nature of the mass. The patient underwent surgical resection of the thrombus and curative treatment of her breast cancer was pursued.

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)0.003
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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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