Teaching frontal sinus anatomy using the frontal sinus masterclass 3‐ D conceptualization model
Bibliographic record
Abstract
OBJECTIVE: Frontal sinus anatomy is complex and often is a difficult subject to both teach and learn. The traditional surgical dogma of "see one, do one, teach one" is impractical and dangerous in the frontal sinus. Based on the building block three-dimensional conceptualization module, the Frontal Sinus Masterclass (FSMC) was created to teach this anatomy. METHODS: Study was performed at two academic centers among second- to fifth-year otolaryngology residents. A pretest assessed knowledge prior to the course. Computed tomography scans of the sinuses were evaluated in triplanar view, and participants attempted to reconstruct the anatomy. Subsequently, the course instructor explained the anatomy using the building block method and showed a short video of the surgical dissection, pointing out relevant anatomy. Cases progressed in anatomical difficulty and inflammatory load. A posttest determined knowledge after the course. RESULTS: Thirty of 50 participating residents completed the pre- and posttests (14 junior, 16 senior residents). Correct identification of the frontal sinus drainage pathway increased from 42% to 63% correct (P = 0.054). Anatomical assessment increased from 61% to 68% correct (P = 0.047), and overall assessment increased from 52% to 66% correct (P = 0.016). CONCLUSION: Objectively, participants of the FSMC expanded on their ability to recognize cells of the frontal recess on CT scans. Before the class, residents could answer less than half of the answers correctly, and by the end of the class they were answering over two-thirds of these complex questions correctly. Subjectively, participants reported benefit from the course and felt they would be better surgeons. LEVEL OF EVIDENCE: NA. Laryngoscope, 128:1294-1298, 2018.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".