Cone Beam CT Features and Oral Radiologist’s Decision-making ofArrested Pneumatization of the Sphenoid Sinus
Bibliographic record
Abstract
OBJECTIVES: To assess the demographic and radiographic features of arrested pneumatization of the sphenoid sinus (APS) and their influence on the confidence of oral and maxillofacial radiologists (OMFRs) in diagnosing APS. METHODS: Reports of cone beam computed tomography (CBCT) APS were retrieved, and the demographic and radiographic features were retrospectively analyzed. Five OMFRs assessed the CBCT images and their confidence in diagnosing APS. The OMFRs' experience (years), expertise (skull-base CBCT cases/month) and diagnostic confidence level were analyzed for agreement and associations with demographic or radiographic features. RESULTS: Of 29 APS cases, 17 (58.6%) were females, and the mean age was 29.9±19 years. Twenty cases (69.0%) presented unilaterally, and 27 (93.1%) involved the sphenoid body. The most common accessory site was the pterygoid process (19, 65.5%). The vidian canal and foramen rotundum were involved in 27 (93.1%) and 17 (58.6%) cases, respectively. Most cases (28, 96.6%) were well-defined, corticated, and showed mixed attenuation. APS diagnostic confidence was higher among the expert OMFRs (72.4%-82.8% vs. 58.6%-62.1%). CONCLUSION: Radiographic features differentiating APS from skull-base tumors were shown on CBCT. The confidence of OMFRs with similar experience in years depended on their frequency of examining CBCT cases involving the skull base.
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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 | 0.020 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".