Point of care ultrasound training for internal medicine: a Canadian multi-centre learner needs assessment study
Bibliographic record
Abstract
Abstract Background Significant gaps currently exist in the Canadian internal medicine point-of-care ultrasound (POCUS) curriculum. From a learner’s perspective, it remains unknown what key POCUS skills should be prioritized. This needs assessment study seeks to establish educational priorities for POCUS for internal medicine residents at five Canadian residency training programs. Methods All internal medicine trainees [postgraduate year (PGY) 1–5] from five internal medicine residency training programs in Canada (n = 598) were invited to complete an online survey on 15 diagnostic POCUS applications, 9 bedside procedures, and 18 POCUS knowledge items. For POCUS applications and procedures, participants were asked how applicable they are to patient care in internal medicine and the participants’ reported skills in those domains. Self-reported knowledge and skills were rated on a 5-point Likert scale, where 1 = very poor and 5 = very good. Applicability was rated, where 1 = not at all applicable and 5 = very applicable. Results A total of 253 of 598 residents (42%) participated in our study. Data from one centre (n = 15) was removed because of low response rate (15%) and significant baseline differences between those trainees and the remaining participants. Of the remaining analyzable data from four training programs (n = 238), participants reported highest applicability to internal medicine for the following applications and procedures: identifying ascites/free fluid [mean applicability score of 4.9 ± standard deviation (SD) 0.4]; gross left ventricular function (mean 4.8 ± SD 0.5) and pericardial effusion (mean 4.7 ± SD 0.5); thoracentesis (mean score 4.9 ± SD 0.3), central line insertion (mean 4.9 ± SD 0.3), and paracentesis (mean 4.9 ± SD 0.3), respectively. Overall reported knowledge/skills was low, with skill gaps being the highest for identifying deep vein thrombosis (mean gap 2.7 ± SD 1.1), right ventricular strain (mean 2.7 ± SD 1.1), and gross left ventricular function (mean 2.7 ± SD 1.0). Conclusions Many POCUS applications and procedures were felt to be applicable to the practice of internal medicine. Significant skill gaps exist in the four Canadian training programs included in the study. POCUS curriculum development efforts should target training based on these perceived skill gaps.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".