Implementation of a Longitudinal POCUS Curriculum in the Core Internal Medicine Residency Program at Dalhousie University
Bibliographic record
Abstract
Background Point-of-care ultrasound (POCUS) has become a useful diagnostic tool across multiple specialties. However, no standardized curriculum is currently in place for Canadian Internal Medicine (IM) residency programs. This report aims to describe the development of a longitudinal POCUS curriculum at Dalhousie University and reports on resident knowledge, confidence, and perceived clinical utility of POCUS also. Methods Residents in the core IM program were invited to complete a POCUS survey and knowledge test in December 2019. The survey evaluated self-reported confidence in acquired POCUS skills and clinical use in practice, whereas the knowledge test evaluated image interpretation skills. Results A total of 34/45 (75.6%) residents participated, who agreed that POCUS training should be a formal component of residency (4.56 ± 0.56). Scores on the knowledge test improved based on time spent in the curriculum, with postgraduate year (PGY) 1s scoring an average of 70.0% (21/30) and PGY3s 82.8% (24.9/30; P = 0.02). Residents reported the strongest confidence in lung imaging for detecting A and B lines (4.10 ± 0.79), pleural effusions (3.92 ± 0.90), and lung sliding (3.89 ± 0.92). Conclusion Dalhousie University is among the first IM programs in Canada to implement a formal longitudinal POCUS curriculum, which has enabled the incremental acquisition of POCUS knowledge, confidence, and clinical utility amongst residents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".