Development of a self‐directed sinonasal surgical anatomy video curriculum: Phase 1 validation
Bibliographic record
Abstract
BACKGROUND: Sinusitis is a common outpatient diagnosis made by physicians and is a reason for referral to otolaryngologists. A foundation in basic sinonasal anatomy is critical in understanding sinus pathophysiology and avoiding complications. Our objective in this study was to develop and to validate a self-directed surgical anatomy video for medical students. METHODS: Two multimedia videos were developed highlighting sinonasal anatomy. In Video 1 we included audio narration and radiologic imaging. Video 2 incorporated highlighted images from a sinus surgery video. An assessment was developed to test sinonasal anatomy landmarks, spatial recognition of structures, and their clinical relevance. An expert panel of rhinologists scored face and content validity of the curriculum videos and assessment. Factor analysis was used to separate questions into face and content validity domains, and a one-sample t test was performed. RESULTS: The panel scored face validity (Videos 1 and 2: 4.4/5) and content validity (Video 1: 4.5/5, 0.83; Video 2: 4.3/5, 0.75) significantly higher than a neutral response. There were no statistical differences for face or content validity between videos. The assessment was rated suitable (29%) or very suitable (57%) for testing basic sinonasal surgical anatomy, and the majority (71%) of respondents agreed (14%) or strongly agreed (57%) that the assessment thoroughly covered the sinus anatomy content with which medical students should be familiar. CONCLUSION: We have developed two videos and an assessment that highlight and test sinonasal anatomy. Future studies will aim to identify whether the use of a self-directed video curriculum improves sinonasal anatomy awareness and whether incorporation of surgical endoscopic videos augments training.
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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 | 0.021 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".