Computed tomography analysis of frontal cell prevalence according to the International Frontal Sinus Anatomy Classification
Bibliographic record
Abstract
BACKGROUND: The International Frontal Sinus Anatomy Classification (IFAC) is an international consensus document published in 2016 to standardize the nomenclature of cells in the region of the frontal recess and frontal sinus. The IFAC was designed to be surgically relevant and anatomically precise. The current study was undertaken to assess the prevalence of the frontal cell variants as defined by the IFAC, as well as to determine the interrater reliability of the IFAC. METHODS: Three independent reviewers examined triplanar nondiseased maxillofacial computed tomography (CT) scans to assess the anatomy of the frontal recess according to the IFAC system. The prevalence of each cell type was assessed and interrater reliability was measured using an intraclass correlation coefficient (ICC). RESULTS: One hundred CT scans (200 sides) were examined. Of the 200 sides, 96.5% contained an agger nasi cell (ICC, 0.82; 95% confidence interval [CI], 0.77-0.86), 30.0% contained a supra agger cell (ICC, 0.89; 95% CI, 0.86-0.92), 20.0% contained a supra agger frontal cell (ICC 0.80; 95% CI 0.74-0.84), 72.0% contained a supra bulla cell (ICC, 0.81; 95% CI, 0.76-0.85), 5.5% contained a supra bulla frontal cell (ICC, 0.71; 95% CI, 0.63-0.77), 28.5% contained a supraorbital ethmoid cell (ICC, 0.78; 95% CI, 0.72-0.83), and 30.0% contained a frontal septal cell (ICC, 0.80; 95% CI, 0.74-0.84). The ICC was good to excellent for identification of all frontal cell types. CONCLUSIONS: This study describes the normative distribution of frontal recess cells in a nondiseased population according to IFAC and demonstrates favorable interrater reliability of the classification system.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".