Multi-Residency Implementation of an Online Movement Disorders Curriculum Based on Real Patient Videos
Bibliographic record
Abstract
Background: In-person didactic education in residency has numerous challenges including inconsistent availability of faculty and residents, limited engagement potential, and non-congruity with clinical exposure. Methods: An online curriculum in movement disorders was implemented across nine neurology residency programs (six intervention, three control), with the objective to determine feasibility, acceptability, and knowledge growth from the curriculum. Residents in the intervention group completed ten modules and a survey. All groups completed pre-, immediate post-, and delayed post-tests. Results: Eighty-six of 138 eligible housestaff (62.3%) in the intervention group completed some modules and 74 completed at least half of modules. Seventy-four, 49, and 30 residents completed the pre-, immediate post-, and delayed post-tests respectively. Twenty-five of 42 eligible control residents (59.5%) completed at least one test. Mean pre-test scores were not significantly different between groups (6.33 vs. 6.92, p = 0.18); the intervention group had significantly higher scores on immediate post- (8.00 vs. 6.79, p = 0.001) and delayed post-tests (7.92 vs. 6.92, p = 0.01). Residents liked having a framework for movement disorders, appreciated the interactivity, and wanted more modules. Residents completed the curriculum over variable periods of time (1-174 days), and at different times of day. Discussion: This curriculum was feasible to implement across multiple residency programs. Intervention group residents showed sustained knowledge benefit after participating, and residents took advantage of its flexibility in their patterns of module completion. Similar curricula may help to standardize certain types of clinical learning and exposure across residency programs. Highlights: Interactive online tools for resident didactic learning are valuable to residents. Residents learn from interactive online curricula, find the format engaging, and take advantage of the flexibility of online educational tools. Beginner learners appreciate algorithms that help them to approach a new topic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".