Experimental study: using the continuous wave Doppler spectrum technique to detect left atrial pressure
Bibliographic record
Abstract
Background: This study sought to investigate the accuracy of estimating left atrial pressure (LAP) using the continuous wave Doppler spectrum of mitral regurgitation. Methods: Dog models of left atrial hypertension with mitral regurgitation were established with disposable biopsy forceps and the injection of melamine formaldehyde resin microsphere suspension. A total of 40 models of left atrial hypertension with different hemodynamic statuses were established by injecting either esmolol or dobutamine in which the spectrums of mitral regurgitation were clear and the regurgitation velocity exceeded 3.5 m/s. The continuous wave Doppler spectrums of mitral regurgitation were recorded and analyzed to estimate left atrial pressure (LAPECHO). The mean left atrial pressure (LAPC-MEAN), the isovolumic diastolic left atrial pressure (LAPC-IVRT), the maximum left atrial pressure (LAPC-MAX), and the minimum left atrial pressure (LAPC-MIN) were also measured using the catheter method in the same cardiac cycle. Results: The LAPECHO (mean ± standard deviation; 11.77±4.36 mmHg) was correlated with the LAPC-MEAN (11.51±4.77 mmHg; r=0.887, P=0.000), but the difference was not statistically significant (P=0.459). The LAPECHO was correlated with the LAPC-IVRT (12.16±4.72 mmHg; r=0.883, P=0.000), but the difference was not statistically significant (P=0.271). There was a correlation between the LAPC- MEAN and the LAPC-IVRT (r=0.987, P=0.000), and the difference was statistically significant (P=0.000). Conclusions: This study suggests that the ultrasound evaluation of LAP correlates well with LAP measured using the gold standard catheter method, and is a simple, convenient, non-invasive method to quantitatively estimate LAP. This method is promising, but further large-scale animal experiments and clinical studies need to be conducted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".