Comparing Central Aortic Pressures Obtained Using a SphygmoCor Device to Pressures Obtained Using a Pressure Catheter
Bibliographic record
Abstract
BACKGROUND: This study compared aortic pressures estimated using a SphygmoCor XCEL PWA device (ATCOR, Naperville, IL) noninvasively with aortic pressures obtained using pressure catheters during catheterization procedures and analyzed the impact of a linear-fit function on the estimated pressure values. METHODS: One hundred and thirty-six patients scheduled for cardiac catheterization procedure were enrolled in IRB approved studies. Catheterization procedures were performed according to standard-of-care to acquire aortic pressure measurements. Immediately after the catheterization procedure with the pressure catheters removed, while the patients were still in the catheterization laboratory, central aortic pressures were estimated with the SphygmoCor device (using its inbuilt transfer function). The error between measured and estimated aortic pressures was evaluated using Bland-Altman analysis (n = 93). A linear-fit was performed between the measured and estimated pressures, and using the linear equation the error measurements were repeated. A bootstrap analysis was performed to test the generalizability of the linear-fit function. In a subset of cases (n = 13), central aortic pressure values were also obtained using solid-state high-fidelity catheters (Millar, Houston, TX), and the error measurements were repeated. RESULTS: The magnitude of errors between the measured and estimated aortic pressures (mean errors >6.4 mm Hg; mean errors >8.0 mm Hg in the subset) were reduced to less than 1 mm Hg after using the linear-fit function derived in this study. CONCLUSIONS: For the population examined in this study, the SphygmoCor data must be used with the linear-fit function to obtain aortic pressures that are comparable to the measurements obtained using pressure catheters. CLINICAL TRIALS REGISTRATION: Trial Numbers NCT03243942 and NCT03245255.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".