Imaging skills for transthoracic echocardiography in cardiology fellows: The value of motion metrics
Bibliographic record
Abstract
BACKGROUND: Proficiency in transthoracic echocardiography (TTE) requires an integration of cognitive knowledge and psychomotor skills. Whereas cognitive knowledge can be quantified, psychomotor skills are implied after repetitive task performance. We applied motion analyses to evaluate psychomotor skill acquisition during simulator-based TTE training. METHODS AND RESULTS: During the first month of their fellowship training, 16 cardiology fellows underwent a multimodal TTE training program for 4 weeks (8 sessions). The program consisted of online and live didactics as well as simulator training. Kinematic metrics (path length, time, probe accelerations) were obtained at the start and end of the course for 8 standard TTE views using a simulator. At the end of the course TTE image acquisition skills were tested on human models. After completion of the training program the trainees reported improved self-perceived comfort with TTE imaging. There was also an increase of 8.7% in post-test knowledge scores. There was a reduction in the number of probe accelerations [median decrease 49.5, 95% CI = 29-73, adjusted P < 0.01], total time [median decrease 10.6 s, 95% CI = 6.6-15.5, adjusted P < 0.01] and path length [median decrease 8.8 cm, 95% CI = 2.2-17.7, adjusted P < 0.01] from the start to the end of the course. During evaluation on human models, the trainees were able to obtain all the required TTE views without instructor assistance. CONCLUSION: Simulator-derived motion analyses can be used to objectively quantify acquisition of psychomotor skills during TTE training. Such an approach could be used to assess readiness for clinical practice of TTE.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| 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".