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Utility of Cardiac CT and MRI for the Diagnosis and Preoperative Assessment of Cardiac Paraganglioma

2009· article· en· W2097771519 on OpenAlexaff
Abdullah A. Alghamdi, Tarang N. Sheth, Zbigniew Manowski, Ofei F. Djoleto, Gopal Bhatnagar

Bibliographic record

VenueJournal of Cardiac Surgery · 2009
Typearticle
Languageen
FieldMedicine
TopicAdrenal and Paraganglionic Tumors
Canadian institutionsTrillium Health Centre
Fundersnot available
KeywordsMedicineAsymptomaticRadiologyCardiac TumorsParagangliomaMagnetic resonance imagingSurgical planningArteryCardiac function curveCardiac magnetic resonance imagingAngiographyCardiologySurgeryHeart failure

Abstract

fetched live from OpenAlex

BACKGROUND: Cardiac paragangliomas are rare cardiac tumors that are usually benign. Surgical excision can be curative. METHODS: We report a case of 39-year-old male who, during the work up of acute coronary syndrome with coronary angiography, cardiac computed tomography (CT) and magnetic resonance imaging (MRI), was found to have cardiac paraganglioma. RESULTS: The tumor was intrapericardial, arising at the level of proximal left anterior descending artery. The tumor was completely resected and the postoperative course was uneventful. At 3-months follow-up the patient was asymptomatic with normal ventricular function. CONCLUSION: Cardiac CT and MRI are valuable in characterizing and preoperative planning of primary cardiac paragangliomas.

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)0.000
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 score0.087
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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)0.0000.000

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 head0.029
GPT teacher head0.316
Teacher spread0.288 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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