Point-of-care ultrasound as a competency for general internists: a survey of internal medicine training programs in Canada
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: 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.
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 | 0.045 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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