An Evaluation of the Utility of Postmortem Computed Tomography in the Diagnosis of Lethal Coronary Artery Atherosclerosis and Hypertensive Heart Disease
Bibliographic record
Abstract
Autopsy is regarded as the gold standard for evaluation of human remains in the forensic pathology setting. Amongst the most common causes of death in any medical examiner jurisdiction are atherosclerotic cardiovascular disease and/or hypertensive cardiovascular disease. Practical experience shows that noncontrast “screening” postmortem computed tomography (PMCT) does not accurately document or diagnose lethal coronary artery atherosclerosis, nor does it allow for the diagnosis of hypertensive cardiovascular disease. One hundred adult forensic autopsies were selected from an 18-month period for this blinded, retrospective case-controlled study. The cases were composed of two age- and sex-matched groups by cause of death: 1) those due to hypertensive and atherosclerotic cardiovascular disease and 2) those due to other causes. Two forensic pathologists, blinded to the cause of death, reviewed pre-autopsy PMCT scans of the chest and recorded the presence or absence of clinically significant coronary artery stenosis, myocardial pathology (including left ventricular hypertrophy and myocardial infarction), cardiomegaly, and coronary artery calcium deposition. The same set of data was obtained from the corresponding autopsy reports. Results of the PMCT interpretations were compared with the results obtained from autopsy. Assessment of PMCT scans resulted in missing all 56 cases with severe coronary artery atherosclerosis, 50 cases with myocardial pathology, and 44 cases with cardiomegaly. Although PMCT did prove sensitive and superior for the detection of coronary artery calcification, this finding is clinically insignificant and of limited to no value to the vast majority of cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".