Non-Immersive Virtual Cardiac Auscultation Interactions Employing a 3D Printed Stethoscope
Bibliographic record
Abstract
Cardiac auscultation is an examination procedure that allows a medical professional to listen to heart murmurs while employing a stethoscope. Current auscultation training is seeing a trend in favour of employing diagnostics equipment, which in comparison to a stethoscope-based examination, provides a comprehensive visualization of the heart. However, despite employing advance auscultation equipment, heart diagnosis skills with the stethoscope are declining. The loss of such skills has caused concern amongst physicians, as the stethoscope remains widely available and affordable worldwide, unlike other specialized tools. Currently, simulation is being employed to improve the diagnosis of heart conditions since mastering cardiac auscultation skills requires extensive training due to the complexity of the heart murmurs. Moreover, high fidelity medical simulators provide realism and accuracy at a high cost, thus providing suitable training but limited practices depending on the number of trainees. In this paper, we present a 3D printed stethoscope user input device to be used in a non-immersive virtual cardiac auscultation examination training mobile application. We also describe the results of a preliminary usability study that involved participants using a 3D printed stethoscope, touch screen interactions, and a simulation manikin to examine a virtual patient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".