Type IV optic nerve and Onodi cell: is there a risk of injury during sphenoid sinus surgery?
Bibliographic record
Abstract
Objective: This study aims to determine the prevalence and types of Onodi cells through computed tomography and investigate the relationship between Onodi cell and the surrounding structures, paying particular attention to the risky proximity to the optic nerve canal. Methods: In this study, 430 computed tomography scans of paranasal sinuses were analysed to establish the prevalence and different types of Onodi cells. Furthermore, the relationship between Onodi cell and different patterns of sphenoid sinus pneumatisation and surrounding structures were investigated. Special attention was paid to the relationship between Onodi cell and the optic nerve canal, particularly in cases when the optic nerve canal was bulging by more than 50% into the Onodi cell (Type IV). Results: The Onodi cell was detected in 21.6% of cases, with the most common being Type I (48.5% right, 54.3% left). Type IV bulging of the optic nerve canal into the Onodi cell was observed in 47.1% of cases on the right side, 41.2% on the left side and bilateral in 11.7% of cases. Conclusions: In our series, we observed a high prevalence of Type IV optic nerve bulging into the Onodi cell. For this reason, we suggest that clinicians should always try to identify it in a pre-operative setting with computed tomography to avoid catastrophic consequences during endoscopic sinus surgery approaching the sphenoid area.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".