Focused Cardiac Ultrasound Curriculum for Internal Medicine Residents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Focused cardiac ultrasound (FCU) is a safe and efficient diagnostic intervention for internal medicine physicians. FCU is a highly teachable skill, but is used in routine cardiac assessment in only 20% of surveyed training programs. We developed an FCU curriculum for internal medicine residents and an assessment tool to evaluate the impact of the curriculum on trainee knowledge and confidence. Methods: Internal medicine residents rotating through clinical cardiology services underwent 30 minutes of didactic and 60 minutes of hands-on teaching on acquisition and interpretation of FCU. A 20 item pre and post-curriculum online survey was administered (November 2018-December 2019) to assess confidence and knowledge in FCU. Results: 79 of 116 (68%) residents completed the pre-survey and 50 completed the post-survey, of whom 34 received the curriculum. The mean change in confidence score in those who received versus did not receive the curriculum was 0.99 versus 0.39 (p=0.046) on a 5-point Likert scale. Among 33 residents who had paired pre- and post-surveys the mean change in confidence score was 1.2 versus 0.85 (p<0.001) in those who received versus did not receive the curriculum. The mean increase in knowledge score was 13% versus 7% respectively (p<0.0001). Conclusions: We instituted a novel curriculum for internal medicine residents to gain experience in image acquisition and interpretation. Both confidence and knowledge in FCU improved following the curriculum, indicating that this is a highly teachable skill. Additional analysis of the of the FCU study images will be useful for informing future interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it