Point-of-care ultrasound as a competency for general internists: a survey of internal medicine training programs in Canada
Bibliographic record
Abstract
BACKGROUND: Point-of-care ultrasound (POCUS) is increasingly used on General Internal Medicine (GIM) inpatient services, creating a need for defined competencies and formalized training. We evaluated the extent of training in POCUS and the clinical use of POCUS among Canadian GIM residency programs. METHOD: Internal Medicine trainees and GIM Faculty at the University of Toronto were surveyed on their clinical use of POCUS and the extent of their training. We separately surveyed Canadian IM Program Directors and Division Directors on the extent of POCUS training in their programs, barriers in the implementation of POCUS curricula, and recommendations for POCUS competencies in IM. RESULTS: A majority of IM trainees (90/118, 76%) and GIM Faculty (15/29, 52%) used POCUS clinically. However, the vast majority of resident (111/117, 95%) and GIM Faculty (18/28, 64%) had received limited training. Of the Program Leaders surveyed, half (9/17, 53%) reported POCUS clinical use by their trainees; however only one quarter (4/16, 25%) reported offering formal curricula. Most respondents agreed that POCUS training should be incorporated into IM residency curricula, specifically for procedural guidance. CONCLUSIONS: A considerable discrepancy exists between the clinical use of POCUS and the extent of formal training among Canadian IM residents and GIM Faculty. We propose that formalized POCUS training should be incorporated into IM residency programs, GIM fellowships, and Faculty development sessions, and identify POCUS skills that could be incorporated into future IM curricula.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".