Insonation versus Auscultation in Valvular Disorders: Is Aortic Stenosis the Exception? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Handheld echocardiography is being proposed as the fifth pillar of bedside physical cardiovascular examination (PE) and is referred to as insonation. Although there is emerging consensus that insonation is superior to PE for diagnosis of various cardiac conditions, superiority has not been consistently demonstrated for various valvular heart disease (VHD) lesions. The objective of this review is to systematically review the accuracy of insonation and auscultation in published literature for detection of common VHD. METHODS: An extensive literature search across three commonly used public databases allowed comparison of diagnostic characteristics of insonation and auscultation for common VHD including aortic stenosis, mitral regurgitation, aortic regurgitation, tricuspid regurgitation. Sensitivity, specificity, and accuracy of insonation and auscultation for the detection of these VHD lesions were extracted for further analysis. The quality of evidence was assessed according to Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. RESULTS: Eight hundred eighty studies were screened, and seven observational studies were selected for full analysis. Due to heterogeneity of data, this study was not amenable to meta-analysis. Insonation was superior to auscultation for the detection of all regurgitant lesions, but there was no significant difference in diagnostic ability of the two strategies for detection of aortic stenosis. CONCLUSIONS: Compared to auscultation, insonation, in its currently available form, is a superior diagnostic tool for regurgitant lesions. However, insonation fails to improve upon auscultation for recognition of aortic stenosis. This limitation is likely due to absence of spectral Doppler and inability of HE to assess transvalvular velocity and gradient.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".