Characterizing the biomechanical differences between novice and expert point‐of‐care ultrasound practitioners using a low‐cost gyroscope and accelerometer integrated sensor: A pilot study
Bibliographic record
Abstract
Abstract Introduction Point‐of‐care ultrasound (POCUS) has become an important diagnostic tool in acute care medicine; however, little is known about the biomechanical differences between novice and expert practitioners. Methods A low‐cost ($50 CAD) gyroscope and accelerometer integrated sensor was assembled and affixed to an ultrasound probe. Seventeen participants, nine novices and eight experts, were recruited to perform three abdominal and four cardiac scans on a standardized patient. Participant demographics, time per scan, average acceleration, average angular velocity, decay in acceleration and angular velocity over time, and frequency of probe movements were analyzed. Video capture with blinded video review was scored. Results On video review, experts had higher image optimization and acquisition scores for both abdominal and cardiac scans. Experts had shorter scan times for abdominal (7 s vs. 26 s, p = 0.003) and cardiac (11 s vs. 26 s, p < 0.001) scans. There was no difference in average acceleration ( g ) between novices and experts performing abdominal (1.02 vs. 1.01, p = 0.50) and cardiac (1.01 vs. 1.01, p = 0.45) scans. Experts had lower angular velocity (°/s) for abdominal scans (10.00 vs. 18.73, p < 0.001) and cardiac scans (15.61 vs. 20.33, p = 0.02) There was a greater decay in acceleration over time for experts performing cardiac scans compared to novices (−0.194 vs. −0.050, p = 0.03) but not for abdominal scans or when measuring angular velocity. The frequency of movements (Hz) was higher for novices compared to experts for abdominal (16.68 vs. 13.79, p < 0.001) and cardiac (17.60 vs. 13.63, p = 0.002) scans. Discussion This study supports the feasibility of a low‐cost gyroscope and accelerometer integrated sensor to quantify the biomechanics of POCUS. It may also support the concept of “window shopping” as a method by which experts obtain abdominal and cardiac views, where sliding is used to find an acoustic window, then smaller rocking and tilting probe movements are used to refine the image.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".