Evaluation of myocardial CT perfusion in patients presenting with acute chest pain to the emergency department: comparison with SPECT-myocardial perfusion imaging
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.
Bibliographic record
Abstract
OBJECTIVE: To determine whether evaluation of resting myocardial CT perfusion (CTP) from coronary CT angiography (CTA) datasets in patients presenting with chest pain (CP) to the emergency department (ED), might have added value to coronary CTA. DESIGN, SETTING: 76 Patients (age 54.9 y±13; 32 (42%) women) presenting with CP to the ED underwent coronary 64-slice CTA. Myocardial perfusion defects were evaluated for CTP (American Heart Association 17-segment model) and compared with rest sestamibi single-photon emission CT myocardial perfusion imaging (SPECT-MPI). CTA was assessed for >50% stenosis per vessel. RESULTS: CTP demonstrated a sensitivity of 92% and 89%, specificity of 95% and 99%, positive predictive value (PPV) of 80% and 82% and negative predictive value (NPV) of 98% and 99% for each patient and for each segment, respectively. CTA showed an accuracy of 92%, sensitivity of 70.4%, specificity of 95.5%, PPV 67.8%, and NPV of 95% compared with SPECT-MPI. When CTP findings were added to CTA the PPV improved from 67% to 90.1%. CONCLUSIONS: In patients presenting to the ED with CP, the evaluation of rest myocardial CTP demonstrates high diagnostic performance as compared with SPECT-MPI. Addition of CTP to CTA improves the accuracy of CTA, primarily by reducing rates of false-positive CTA.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it