E-learning to teach medical students about acute otitis media: A randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: Acute otitis media (AOM) is extremely prevalent among children but its diagnosis remains challenging. Our primary objective was to measure the impact of an e-learning module on medical students' accuracy in diagnosing paediatric AOM. METHODS: This randomized controlled trial was performed at a single tertiary care paediatric emergency department (ED). Medical students on their paediatric rotation were randomized to a locally developed e-learning module or a small-group lecture on AOM. They then had to examine at least 10 ears of patients at risk for AOM. The primary outcome was diagnostic accuracy and secondary outcomes included knowledge test scores and learning modality preference. RESULTS: Between May 2017 and September 2018, 201 medical students were randomized. Eighty-three evaluated at least 10 ears and were included in the primary analysis. Diagnostic accuracies (76.5% for the e-learning group versus 76.4% for the lecture group, difference of 0.1%; 95%CI: -6.2 to 6.4%) and post-test scores (difference of 0.5/20 points; 95%CI: -0.8 to 1.2/20 points) were similar between the groups. Sixty-two per cent of participants preferred the e-learning module to the lecture, while 15% had no preference. CONCLUSIONS: Diagnostic accuracy for AOM was similar between students exposed to an e-learning module or a small-group lecture. E-learning was the preferred learning modality.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".