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Abstract 10426: An Interesting Presentation of Angina Due to Left Posterolateral to Right Posterolateral Coronary Artery Fistula

2022· article· en· W4380793578 on OpenAlexaboutno aff
Vivek Nadipalli, Ahmed Mahmood, Muhammad Uzair Lodhi, Bilal Hussain, Sohail Khan, Christel Cuevas, Thomas Alexander

Bibliographic record

VenueCirculation · 2022
Typearticle
Languageen
FieldMedicine
TopicCoronary Artery Anomalies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyInternal medicineRight coronary arteryCanadian Cardiovascular SocietyFistulaCoronary stealAnginaCoronary arteriesRadiologyArteryMyocardial infarctionCoronary angiography

Abstract

fetched live from OpenAlex

Background: Arterial-to-arterial fistula between right posterolateral and left posterolateral coronary arteries is rare and available literature on it is scarce. Case Presentation: A 60-year-old male with type 2 diabetes mellitus and dyslipidemia presented with shortness of breath and chest tightness. Given his symptoms and poor functional status, myocardial perfusion imaging with regadenoson was performed. The myocardial perfusion imaging study was abnormal with evidence of ischemia in the inferoseptal myocardium. The patient proceeded to the cardiac catheterization lab for selective right and left coronary angiography. Contrast injection revealed non-obstructive coronaries; however, an arterial-to-arterial fistula between the right posterolateral (RPL) branch and left posterolateral (LPL) branch was present (Figure 1A and 1B). Further characterization of the fistula showed the presence of left-to-right and right-to-left filling, indicating a LPL to RPL coronary artery fistula (CAF). The patient was managed conservatively with aspirin, statin, and anti-anginal therapy. Conclusion: There remains a need for diagnostic and therapeutic guidelines to address CAF. Conservative treatment is an option for small CAFs and serves as a potential alternative to catheter-based and surgical closure.

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)0.000
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 score0.291
Threshold uncertainty score0.903

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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