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Record W4302007627 · doi:10.1155/2022/4591024

Aortosternal Venous Compression: A Review of Two Cases

2022· review· en· W4302007627 on OpenAlexaff
Victoria Giglio, Zain Badar, Yasovineeth Bhogadi, Brian van Adel, Gordon Yip

Bibliographic record

VenueCase Reports in Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicVascular anomalies and interventions
Canadian institutionsHamilton General HospitalUniversity Health NetworkUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineBrachiocephalic veinRadiologyVenographyCompression (physics)Superior vena cavaSurgeryThrombosis

Abstract

fetched live from OpenAlex

Aortosternal venous compression (AVC) is a rare venous compression syndrome that involves brachiocephalic venous compression due to its positioning between the sternum and the aorta. One of the features of AVC involves compression of the left innominate vein with variability in luminal caliber on inspiration and expiration. Imaging modalities such as computed tomography (CT) examination can aid in initial diagnosis; however, venography can be utilized for confirmatory diagnosis due to its higher specificity during the inspiratory and expiratory phases. Through findings demonstrated during venography, we herein present two cases of confirmed AVC secondary to an aberrant right subclavian artery. Characteristic imaging features in the diagnosis of AVC and its etiology are discussed.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.396
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.153
GPT teacher head0.462
Teacher spread0.309 · 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 designCase report
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

Citations3
Published2022
Admission routes1
Has abstractyes

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