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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 OpenAlex
Quynh Nguyen, Xiya Ma, Dominique Vervoort, Jessica G.Y. Luc

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
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