Sonoist: An Innovative Peer Ultrasound Learning Initiative on Canadian Teaching Hospital Wards
Bibliographic record
Abstract
Background: Students usually learn point-of-care ultrasound (PoCUS) on standardized patients, thus lacking opportunities to correlate their ultrasound findings with clinical abnormalities. Sonoist is a student-led initiative aimed at improving ultrasound training with peer-teaching and real patients. We describe here a pilot project of Sonoist, its implementation and evaluation. Methods: Sonoist was developed by Independent-Practitioner-certified medical students who teach their peers how to scan patients with abnormal clinical findings, then correlating their ultrasound findings with the physical examination. From May 2019 to February 2020, seven sessions were held, with a sessional average of 3 participants and 3 patients scanned. We collected survey data on ultrasound knowledge, participants’ perceived self-improvement, and general comments. Results were grouped by prior ultrasound training (novice n=8, experienced n=12) and year of study (1-4). Results: 20/23 completed the survey. An increase in ultrasound skill was perceived by 100% of novices and 66.7% of experienced learners. Knowledge about clinical indications for PoCUS improved in 80% of novice and 81% of experienced students; sonographic knowledge improved in 69% of novices and 81.3% of experienced learners. All novices and 91.7% of experienced learners reported that learning ultrasound was useful for correlating with physical exam and clinical diagnosis. All novices and 83% of experienced students preferred peer-to-peer teaching. Conclusion: Peer-to-peer PoCUS teaching improved medical students’ sonographic and clinical knowledge, and is perceived as useful by students. A combination of early clinical exposure and a less stressful environment from peer teaching may contribute to these results.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".