Influence of Differential Calcification in the Descending Thoracic Aorta on Aortic Pulse Pressure
Bibliographic record
Abstract
PURPOSE: Multiple studies have shown pulse pressure (PP) to be a strong predictor of aortic calcification. However, no studies are available that correlate PP with aortic calcification at the segmental level. METHODS: We identified 37 patients with aortic PP measured during cardiac catheterization. Their noncontrast chest computed tomography scans were evaluated for the presence of calcium in different segments (ascending aorta, arch of aorta [arch], descending aorta) and quantified. Patients with calcification (Calcified Group A) were compared against patients without calcification (Noncalcified Group B) in terms of PP, calcification and compliance. RESULTS: The mean of the total calcium score was higher in the descending aorta than the arch or ascending aorta (691 vs 571 vs 131, respectively, P<0.0001). PP had the strongest correlation with calcification in the descending aorta (r=0.47, P=0.004). Calcified Group A had a much higher PP than Noncalcified Group B, with the greatest difference in the descending aorta (20 mmHg, P<0.0001), lesser in the ascending aorta (10 mmHg, P=0.12) and the least in the arch (5 mmHg, P=0.38). Calcified Group A patients also had much lower compliance than Noncalcified Group B patients, with the greatest difference among groups seen in the descending aorta (0.7 mL/mmHg, P=0.002), followed by the ascending aorta, then arch. CONCLUSIONS: These are the first data to evaluate the relative impact of aortic segments in PP. Finding the greatest amount of calcification along with greatest change in PP and compliance in the descending aorta makes a case that the descending aorta plays a major role in PP as compared to other segments of the thoracic aorta.
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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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".