Online vs in-person musculoskeletal ultrasound course: a cohort comparison study
Bibliographic record
Abstract
BACKGROUND: Point-of-care musculoskeletal (MSK) ultrasound (US) courses are typically held in-person. The COVID-19 pandemic guidelines forced courses to switch to online delivery. To determine this impact, we conducted an observational cohort study, comparing homework completion and image quality between an Online and a historical In-person cohort. METHODS: The In-person (n = 27) and Online (n = 24) cohorts attended two learning sessions spaced six months apart. The course content was the same, while the process of delivery differed. As homework, participants submitted US images biweekly for up to five months after each session. Expert faculty provided written feedback to all participants, and two independent reviewers rated the image quality for a subset of participants in each group who had completed at least 70% of their homework (In-person, n = 9; Online, n = 9). Participants self-reported their satisfaction through post-course evaluation. RESULTS: 63% of In-Person and 71% of Online cohort participants submitted their homework images. We observed no differences in the mean amount of homework images submitted for In-person (M = 37.3%, SD = 42.6%) and Online cohorts (M = 48.1%, SD = 38.8%; p > 0.05, Mann-Whitney U Test). At course end, the cohorts did not differ in overall image quality (p > 0.05, Wilcoxon Signed-rank Test). All participants reported high levels of satisfaction. CONCLUSIONS: A convenience sample of participants attending a basic MSK US course in-person and online did not differ statistically in homework completion, quality of submitted US images, or course satisfaction. We add to literature suggesting online learning remains a viable option post-pandemic.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".