Teaching Residents Frontal Sinus Anatomy Using a Novel 3-Dimensional Conceptualization Planning Software-Based Module
Bibliographic record
Abstract
BACKGROUND: The Frontal Sinus Masterclass (FSMC) is an effective method for teaching frontal sinus anatomy. A third party developed new software using the same 3-dimensional building block concept. The authors sought to determine whether the use of the software for the educational module yielded similar results to the original FSMC, which used wooden blocks. METHODS: The study was performed at a sinus course for residents. A precourse test assessed the ability to decipher frontal sinus anatomy prior to the course. Computed tomography (CT) scans of the sinuses were evaluated in triplanar view, and participants attempted to reconstruct the anatomy using the new software. Subsequently, the course instructor explained the anatomy and showed a short corresponding surgical video of the frontal sinusotomy. Cases progressed in anatomical difficulty and inflammatory load. A postcourse test determined knowledge after the course. RESULTS: In sum, 50 residents completed the pre- and postcourse tests. Overall scores increased from 60.5% to 65.2% ( P = .004). Subanalysis also demonstrated improved ability to locate the frontal drainage pathway from 32.5% to 46% ( P = .011) and label the frontal recess cell structures from 64% to 67.6% ( P = .045). CONCLUSION: There is minimal literature on proven methods for teaching frontal sinus anatomy. Objectively, participants of the modified FSMC simulation training using new software improved their ability to recognize cells of the frontal recess on CT scans. They especially exhibited better localization of the frontal sinus drainage pathway. Subjectively, participants reported benefit from the course and felt they would be better surgeons.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".