Diagnostic Accuracy of Sex-Specific Chest CT Measurements Compared With Cardiac MRI Findings in the Assessment of Cardiac Chamber Enlargement
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to establish sex-specific chest CT measurement thresholds for detection of cardiac chamber enlargement with cardiac MRI as the reference standard. MATERIALS AND METHODS: Consecutive patients who underwent contrast-enhanced chest CT (64- or 320-MDCT) and cardiac MRI within a 7-day interval between August 2006 and August 2016 were included in this retrospective study (n = 217; 115 men, 102 women; mean age, 52.8 ± 15.8 years). Measurements were performed on axial CT images to evaluate right atrial (RA), right ventricular (RV), left atrial (LA), and left ventricular (LV) chamber size. The presence of chamber enlargement (RAE, RVE, LAE, and LVE) was established with cardiac MRI as the reference standard. ROC analysis was performed. Optimal sex-specific CT measurement thresholds were identified that ensured specificity of 90% or greater and maximized sensitivity. RESULTS: The prevalence of chamber enlargement in men was 26% for RAE, 11% for RVE, 40% for LAE, and 24% for LVE. In women the prevalence was 16% for RAE, 15% for RVE, 27% for LAE, and 12% for LVE. The following CT measurement thresholds were optimal: for RAE, RA transverse diameter ≥ 67 mm for men (AUC, 0.825) and ≥ 64 mm for women (AUC, 0.926); for RVE, RV transverse diameter ≥ 60 mm for men (AUC, 0.846) and ≥ 57 mm for women (AUC, 0.858); for LAE, LA anteroposterior diameter ≥ 50 mm for men (AUC, 0.795) and ≥ 45 mm for women (AUC, 0.841); for LVE, LV transverse diameter ≥ 58 mm for men (AUC, 0.917) and ≥ 53 mm for women (AUC, 0.840). CONCLUSION: Cardiac chamber enlargement can be identified with high specificity and reasonable sensitivity on axial chest CT images by use of sex-specific measurement thresholds.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".