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Record W4286699050 · doi:10.1177/15569845221107011

Management Strategies for Descending Thoracic Aortic Thrombus: A Review of the Literature

2022· review· en· W4286699050 on OpenAlexaff
Quynh Nguyen, Xiya Ma, Dominique Vervoort, Jessica G.Y. Luc

Bibliographic record

VenueInnovations Technology and Techniques in Cardiothoracic and Vascular Surgery · 2022
Typereview
Languageen
FieldMedicine
TopicAortic Thrombus and Embolism
Canadian institutionsUniversity of British ColumbiaUniversity of TorontoUniversité de MontréalUniversity of Alberta
Fundersnot available
KeywordsMedicineThrombusRadiologyMagnetic resonance imagingPresentation (obstetrics)ModalitiesComputed tomography angiographyMagnetic resonance angiographyComputed tomographyAngiographyCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

Descending thoracic aortic thrombus (DTAT) is an under-recognized source of systemic emboli with potential catastrophic consequences. Imaging modalities such as echocardiography, computed tomography, magnetic resonance imaging, and angiography can help identify and characterize the extent of embolic events. Established guidelines regarding the management of DTAT are currently lacking. Multiple treatment modalities are available; however, the effectiveness of each approach remains to be determined. In this study, we performed a review to examine the clinical presentation, diagnostic methods and findings, and outcomes of various treatment options for patients with DTAT. Medical management is the least invasive and most frequently chosen initial approach, offering a high reported success rate, whereas endovascular therapy can have a role in thrombus exclusion should conservative management fail.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.370
Teacher spread0.329 · 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 designNot applicable
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

Citations13
Published2022
Admission routes1
Has abstractyes

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Same venueInnovations Technology and Techniques in Cardiothoracic and Vascular SurgerySame topicAortic Thrombus and EmbolismFrench-language works237,207