Use of Hand‐motion Analysis to Assess Competence and Skill Decay for Cardiac and Lung Point‐of‐care Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Assessment of competence in technical skills, including point-of-care ultrasound (POCUS), is required before a novice can safely perform the skill independently. Ongoing assessment of competence is also required because technical skills degrade over time, especially when they are infrequently performed or complex. Hand-motion analysis (HMA) is an objective assessment tool that has been used to evaluate competency in many technical skills. The purpose of this study was to demonstrate the feasibility and validity of HMA as an assessment tool for competence in both simple and complex technical skills as well as skill degradation over time. METHODS: This prospective cohort study included 36 paramedics with no POCUS experience and six physicians who were fellowship trained in POCUS. The novices completed a 4-hour didactic and hands-on training program for cardiac and lung POCUS. HMA measurements, objective structured clinical examinations (OSCE), and written examinations were collected for novices immediately before and after training as well as 2 and 4 months after training. Expert HMA metrics were also recorded. RESULTS: Expert HMA metrics for cardiac and lung POCUS were significantly better than those of novices. After completion of the training program, the novices improved significantly in all HMA metrics, knowledge test scores, and OSCE scores. Novices showed skill degradation in cardiac POCUS based on HMA metrics and OSCE scores while lung POCUS image acquisition skills were preserved. Novices deemed competent by OSCE score performed significantly better in HMA metrics than those not deemed competent. CONCLUSION: We have demonstrated that HMA is a feasible and valid tool for assessment of competence in technical skills and can also evaluate skill degradation over time. Skill degradation appears more apparent in complex skills, such as cardiac POCUS. HMA may provide a more efficient and reliable assessment of technical skills, including POCUS, when compared to traditional assessment tools.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".