Enhanced Telemedicine Instruction: Simulations Including eStethoscopes for Virtual Cardiac Assessment
Bibliographic record
Abstract
Increased adoption of telemedicine during the COVID-19 pandemic improved care access to rural and underserved patients. This rapid expansion created a pressing need for educational programs focusing on the virtual physical exam. We developed a simulation-based workshop for medical and physician assistant students to teach and evaluate the cardiopulmonary virtual physical exam. It consisted of (1) readings, (2) a brief didactic, (3) a telemedicine simulation using a digital stethoscope, (4) personalised feedback from faculty, and (5) a group debrief session. The students were evaluated using a standardised rubric based on three of the 20 telemedicine competencies described by the Association of American Medical Colleges (AAMC). Ninety-five students completed the workshop, and over 80% were entrustable or approaching entrustment in each competency. This workshop illustrates a practical approach to teaching and evaluating telemedicine competencies related to data collection and the physical exam.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".