The Master Clinician’s Elective: Integrating Evidence-Based Physical Examination and Point of Care Ultrasonography in Modern Clinical Medicine
Bibliographic record
Abstract
Background: Many internal medicine residency programs have incorporated ultrasonography into their curriculum; however, its integration with physical examination skills teaching at a graduate medical level is scarce. The program’s aim is to create a reproducible elective that combines physical exam and bedside ultrasound as a method for augmenting residents’ knowledge and competence in these techniques with the ultimate goal of improving patient care. Methods: We designed and implemented a two-week elective rotation for senior internal medicine residents, combining evidence-based physical examination with diagnostic bedside ultrasonography. The rotation took place in an inpatient setting at Denver Health Hospital. Program evaluation data was collected data between February 2016 to March 2019. IRB approval was waived. Results: Since its inception in 2016, 19 residents completed the rotation. Residents performed a pre-test and a post-test under direct observation by course faculty. Each resident was measured on the ability to perform pre-determined physical exam and point-of-care ultrasound (POCUS) skills. In the pre-test, participants correctly performed an average of 40% of expected physical exam maneuvers and 32% of expected POCUS skills. At elective conclusion, all participants were effectively able to demonstrate the highest yield physical exam and ultrasound maneuvers. Discussion and Conclusion: An elective designed specifically to integrate POCUS and physical exam modalities improves the ability of resident physicians to utilize both diagnostic modalities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".