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Record W4210758865 · doi:10.1186/s41824-021-00124-z

Complementary role of computed tomography angiography with fixed perfusion defects on SPECT myocardial perfusion imaging

2022· article· en· W4210758865 on OpenAlexaff
Rami M. Abazid, Yasmine Sallam, Jonathan Romsa, James Warrington, Çiğdem Akincioğlu, Sabe De, Nikolaos Tzemos, William C. Vezina

Bibliographic record

VenueEuropean Journal of Hybrid Imaging · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsNOSM UniversityVictoria HospitalLondon Health Sciences Centre
Fundersnot available
KeywordsPerfusionMedicineMyocardial perfusion imagingRadiologyRevascularizationPerfusion scanningMyocardial infarctionAngiographyCardiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: We present this case series exploring the complementary role of coronary computed tomography angiography (CCTA) to SPECT myocardial perfusion imaging (MPI) in the detection of myocardial necrosis. METHODS: A cardiac hybrid imaging database search identified 144 patients with a previous history of ST-segment elevation myocardial infarction treated with coronary revascularization. CCTA and MPI scans were evaluated to determine whether CCTA had an added value to MPI in detecting myocardial necrosis. RESULTS: Five patients with patent stents and/or bypass grafts and both fixed perfusion defects on MPI and sub-endocardial hypo-perfusion on CCTA were identified. The extent and location of the perfusion defects were closely correlated between the CCTA and SPECT MPI images. CONCLUSION: In this series, CCTA and SPECT MPI were found to play a complementary role in the assessment of fixed perfusion defect, with CCTA adding specificity to the diagnosis of myocardial necrosis.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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