Implementation of a “Flipped Classroom” for Neurosurgery Resident Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Engaging residents across a multiyear training spectrum is challenging given the heterogeneity of experience and limited time available for educational activities. A "flipped classroom" model, in which residents prepare ahead of time for mentored topic discussions, has potential advantages. METHODS: We implemented a curriculum consisting of topics distributed across the specialty. Weekly, each resident was randomly assigned to research a specific aspect of an assigned topic appropriate to his or her level of experience: junior residents about what characterizes each clinical entity, midlevel residents about when to intervene, and chief residents about how to administer treatment. Residents completed an anonymous survey 6 months after implementation. Board examination performance was assessed before and after implementation. RESULTS: A total of 12 residents participated in the program. Weekly, 1.75±0.40 hours were spent in preparation, with senior residents reporting less time than junior residents. All residents indicated that the accumulation of experience across 7 years of residency was a major advantage of this program, and all preferred it to lectures. Performance on the board examination significantly increased after implementation (from 316±36 to 468±45, p<0.05). CONCLUSIONS: The flipped classroom is a viable approach to resident education and is associated with increased engagement and improved performance using validated knowledge-assessment tools.
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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 | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it