Proposing the ValvUS approach: integrating bedside tests and ultrasonography for severe valvular heart disease diagnosis
Bibliographic record
Abstract
Valvular heart disease is increasingly prevalent, and bedside confirmation or exclusion of severe disease is needed to enable a rapid and cost-effective diagnostic workup. The physical examination skills of clinicians are insufficient for accurate diagnosis, making complementary tests generally necessary. Despite being commonly requested, electrocardiography and chest radiography present low positive and negative likelihood ratios. Incipient studies involving artificial intelligence have shown promising opportunities to support the diagnosis. In addition, solid current evidence demonstrates that point-of-care ultrasound enhances bedside diagnosis of several cardiovascular conditions. Echocardiographic skills can be acquired after only a few hours of training, which encourages routine bedside use with handling equipment. Despite the routine use of sonography in emergencies, large-scale simplified screening protocols for valvular disease remain lacking. Therefore, improving the accuracy of valvular heart disease diagnosis by integrating all bedside modalities needs to be better understood. We propose a simple, reproducible five-step point-of-care ultrasound protocol for diagnosing valvular heart disease (the ValvUS approach), applicable to all patients. The proposed visual assessment involves evaluating valvular movement, thickness, regurgitant flow, aliasing, and chamber dimensions. This evaluation should be interpreted in the context of traditional clinical probability to ensure the most accurate bedside diagnosis. Typical findings of severe valvular disease on electrocardiography and chest radiography, and particularly on point-of-care ultrasound, may improve the accuracy of bedside diagnosis after clinical assessment in the near future.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".