SpacED POCUS: A Randomized Controlled Trial of an Adaptive Spaced Education POCUS Curriculum for Medical Students
Bibliographic record
Abstract
This study evaluated the effectiveness, retention, engagement, and acceptability of adaptive spaced education (spacED) for improving the accuracy of point of care ultrasound (POCUS) image interpretation by medical students. From July 2022-May 2023, students (n=36) were randomized into two groups and each assigned 50 unique POCUS cases: cardiac/vascular or lung/FAST. Each one served as the control for the other group. We measured effectiveness (% posttest 1 - % pretest), six-month retention (% posttest 2 - % posttest 1), engagement (% cases completed), and acceptability (% would recommend). Twenty-nine students (81%) completed the study. On average, 38.6% of cases were completed over the six-month study period. There was a significant increase in test scores covering FAST (Focused Assessment with Sonography in Trauma) (+18%), lung (+25%), and vascular (+23%, all p<0.01). Six-month FAST and lung scores did not have significant loss (+3% and -10%, p >0.05). Acceptability was high; 96% of students indicated they would participate again. Despite an imperfect case completion rate, for some applications, spacED was an effective, long-lasting, and acceptable method for teaching POCUS interpretation to medical students.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".