P.116 Not everything is what it seems, look closer, think deeper: granulomatosis with polyangiitis
Bibliographic record
Abstract
Background: Granulomatosis with polyangiitis (GPA) is a rare disease of unknown cause. The multitude of manifestations presents significant diagnostic challenges. This is a unique case of GPA with sinonasal, airway, skull base, petrous bones, vascular, and brain parenchyma involvement. Methods: We present a case of a 45-year-old female with a several day history of headache and left hearing loss. MRI brain demonstrated a large erosive enhancing soft process in the sinonasal cavity and nasopharynx. Results: She developed new ipsilateral rightward tongue deviation. A second MRI demonstrated disease progression. It showed posterior pharyngeal wall ulceration, involvement of the skull base foramina, petrous bones, and central bony skull erosion. It demonstrated right hemiglossal edema secondary to right hypoglossal nerve compression at the skull base. There was enhancing soft tissue partially encasing the left petrous internal carotid artery and more extensively encasing and narrowing bilateral intradural vertebral arteries with associated brainstem edema secondary to direct mass effect and new left occipital thromboembolic infarct. She underwent a nasopharyngeal biopsy which demonstrated acute inflammation on a background of GPA. Conclusions: There are no pathognomonic imaging characteristics for GPA. By recognizing the common and less-common imaging features, radiologists play a crucial role in both diagnosing and monitoring the disease activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".