Automated extraction of aortic and pulmonary components of the second heart sound for the estimation of pulmonary artery pressure
Bibliographic record
Abstract
The second heart sound, S2, is generally believed to be comprised of aortic (A2) and pulmonary (P2) components. Previously, the normalized splitting interval (NSI) between the A2 and P2 components has been shown to be proportional to the pulmonary artery pressure (PAP). A set of fully automated algorithms based on adaptive modeling of A2/P2 components using chirplets were developed to provide real-time estimates of PAP. The method was tested on 16 pigs which were administered drugs to induce pulmonary hypertension. Simultaneous reference pressure measurements were obtained with a pulmonary artery catheter (PAC). Estimation of PAP in pigs using the new techniques resulted in a correlation coefficient (r) of 0.84 and standard error (SEE) of 9.2 mm Hg. This is in line with echocardiography studies, which have a performance ranging from r=0.69-0.91 and SEE from 5 to 12 mm Hg when compared to PAC measurements. It is also consistent with previous results based on a manual estimation of PAP derived through image processing methods. Based on these findings, this method has the potential to offer continuous noninvasive monitoring of PAP.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".