P.086 Unusual case of Aspergillosis presenting as a skull base lesion
Bibliographic record
Abstract
Background: Fungi are ubiquitous microorganisms. Sinonasal fungal infections range from an acute fulminant to a chronic indolent clinical course. Fungal infections are common in immunocompromised patients, diabetics, and those with hematological malignancies. We present an unusual case of chronic invasive fungal sinusitis presenting as an anterior skull base lesion. Methods: A 42-year-old patient was referred with a history of right-sided proptosis. Prompt CT and MR imaging revealed a large right sinonasal erosive mass, predominantly T2 hypointense with heterogeneous enhancement. It extended into the right anterior skull base and invaded the right frontal lobe. The mass also invaded into the right extra-conal orbital fat, right pterygopalatine fossa, and right sphenopalatine foramen. Results: In view of the imaging findings, a biopsy was performed which confirmed fungal elements and chronic inflammation. Subsequently, a right-sided endoscopic endonasal resection of the sinonasal mass with resection of the right orbital component and debulking of the anterior skull base component was performed. Culture specimen grew aspergillosis. Conclusions: Extra-sinus invasion in fungal sinusitis is not uncommon. These cases may mimic other pathologies, e.g., tumors, with potential delay in treatment. Sound knowledge of the imaging appearances of this entity is imperative to ensure a good outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".