Use of Cardiac Magnetic Resonance Imaging Based Measurements of Inferior Vena Cava Cross-Sectional Area in the Diagnosis of Pericardial Constriction
Bibliographic record
Abstract
PURPOSE: To evaluate the value of cardiac magnetic resonance imaging (MRI)-based measurements of inferior vena cava (IVC) cross-sectional area in the diagnosis of pericardial constriction. METHODS: Patients who had undergone cardiac MRI for evaluation of clinically suspected pericardial constriction were identified retrospectively. The diagnosis of pericardial constriction was established by clinical history, echocardiography, cardiac catheterization, intraoperative findings, and/or histopathology. Cross-sectional areas of the suprahepatic IVC and descending aorta were measured on a single axial steady-state free-precession (SSFP) image at the level of the esophageal hiatus in end-systole. Logistic regression and receiver-operating curve (ROC) analyses were performed. RESULTS: Thirty-six patients were included; 50% (n = 18) had pericardial constriction. Mean age was 53.9 ± 15.3 years, and 72% (n = 26) were male. IVC area, ratio of IVC to aortic area, pericardial thickness, and presence of respirophasic septal shift were all significantly different between patients with constriction and those without (P < .001 for all). IVC to aortic area ratio had the highest odds ratio for the prediction of constriction (1070, 95% confidence interval [8.0-143051], P = .005). ROC analysis illustrated that IVC to aortic area ratio discriminated between those with and without constriction with an area under the curve of 0.96 (95% confidence interval [0.91-1.00]). CONCLUSIONS: In patients referred for cardiac MRI assessment of suspected pericardial constriction, measurement of suprahepatic IVC cross-sectional area may be useful in confirming the diagnosis of constriction when used in combination with other imaging findings, including pericardial thickness and respirophasic septal shift.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".