Implementation of a Progressive Three-Year Point of Care Ultrasound Curriculum for Internal Medicine Residents
Bibliographic record
Abstract
Background Point-of-Care Ultrasound (PoCUS) is an ultrasound examination performed by the clinician to answer a focused question or guide an invasive procedure. Despite gaining popularity and evidence supporting the use of PoCUS, core Internal Medicine (IM) residency programs in Canada have yet to implement a comprehensive PoCUS curriculum. The objective of this study was to create a formal PoCUS curriculum. Methods We conducted a systematic needs assessment with a survey that assessed IM attending and resident comfort, training, and application of PoCUS. We also performed a literature review of selected PoCUS-guided procedures and diagnostics to assess the evidence. A working group analyzed the collected data and designed a graduated 3-year curriculum. Results The needs assessment demonstrated that PoCUS education was both necessary and in high demand. The PoCUS-guided procedures and diagnostics that were identified by the survey to be necessary for IM training were then evaluated by a literature review. Based on the evidence, a progressive 3-year curriculum was created. The working group decided on the method and timing of curriculum delivery. Conclusion McMaster University is the first IM residency program to introduce a graduated 3-year curriculum complete with competency assessment and quality assurance.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.005 |
| 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".