Validation of hand motion analysis as an objective assessment tool for the Focused Assessment with Sonography for Trauma examination
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasonography is a standard part of trauma assessments, but there are no objective tools to assess proficiency and ensure high-quality examinations. Hand motion analysis (HMA) has been validated as a measure of surgical skill but has not previously been applied to ultrasonography. HMA was assessed for construct validity in Focused Assessment with Sonography for Trauma (FAST) performance. METHODS: Two cohorts of 12 expert and 12 novice ultrasonographers performed a FAST examination on a healthy volunteer. Hand motions were recorded with the trakSTAR 3D electromagnetic motion-tracking device (Ascension Technology) and analyzed using our custom-designed Motion Analysis and Recording System (MARS) software. Data were recorded at 240 Hz. Outcomes included time of examination, number of movements, and path length. RESULTS: Time of examination was not different between cohorts (expert, 345.9 seconds; novice, 475.7 seconds; p = 0.12). Total path length of travel was shorter, and the number of discreet movements was less in the expert cohort for the left-hand (18.52 m vs. 28.01 m, p = 0.03, and 109.5 vs. 193.9, p = 0.027, respectively) and the right-hand performance (14.25 m vs. 32.09 m, p < 0.01, and 153.5 vs. 258.5, p = 0.03, respectively) versus the novice cohort. Both total path length traveled and total number of discreet movements were associated with expertise level in logistic regression modeling with areas under the receiver operating characteristic curves of 0.8269 and 0.8205, respectively. CONCLUSION: This is the first study in the medical literature showing HMA as an objective, valid measure of FAST imaging performance. These objective, automated metrics can function as an adjunct measure to assess FAST performance as well as follow progress of and provide feedback to learners to improve future performances. LEVEL OF EVIDENCE: A "diagnostic criteria"-style test where the "diagnosis" is a determination of competence in a care provider, level II.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".