P.032 Cavernous sinus masses: An unusual case and review of the literature
Bibliographic record
Abstract
Background: We present a 67-year-old male with a two-week history of progressive double vision. Past medical history included oropharyngeal SCC, T4N2cM0, post-CCRT, and remote sarcoidosis. Clinically, the patient had multiple cranial nerve palsies affecting bilateral occular motor function. Neuroimaging showed an enhancing mass involving the sella and cavernous sinuses. Whole-body PET showed FDG-avid lesions in the sella and liver. Transsphenoidal biopsy of the sellar mass was obtained for tissue diagnosis. Methods: Details of the case were obtained from the patient’s EMR. Neuroimaging and neuropathology were reviewed with the appropriate subspecialists. A literature search was performed using multiple databases (PubMed, Web-of-Science) and relevant articles were included for review. Results: Sellar mass biopsy confirmed p16+ve SCC, identical to the patient’s known primary malignancy. On review of enhanced skull-base images, there was no evidence of direct tumor extension, favouring hematogenous spread. Conclusions: This case demonstrates the localizing potential of cavernous sinus masses. SCC metastases to the cavernous sinus are rare, and confer a poor prognosis.The presence of a p16 mutation has public health implications, as this mutation demonstrates more frequent and aggressive distant metastatic potential, and as a surrogate marker for high-risk HPV infection, represents a preventable risk-factor for a rapidly increasing cause of head and neck cancer in the Western world.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".