Neuroradiologic manifestations of Erdheim-Chester disease
Bibliographic record
Abstract
Background We describe the neuroradiologic features of a cohort of patients with Erdheim-Chester disease. Methods We assessed patients at Mayo Clinic Rochester between January 1, 1990, and July 31, 2016, with pathologically confirmed Erdheim-Chester disease (n = 53). Results Neuroimaging, including head CT (n = 17), brain MRI (n = 39), orbital MRI (n = 15), and spine MRI (n = 16), was available for 42 participants. Median age at diagnosis was 55 years (interquartile range 46–66) with higher male prevalence (33:20). Neurologic symptoms were identified in 47% (25/53); BRAFV600E mutation in 58% (15/26). Median follow-up was 2 years (range 0–20) with 18 patients deceased. Radiologic disease evidence was seen in dura (6/41), brainstem (9/39), cerebellum (8/39), spinal cord (2/16), spinal epidura (2/16), hypothalamic-pituitary axis (17/39), and orbits (13/42). T2 white matter abnormalities (Fazekas score ≥1) were present in 21/34 patients. Diabetes insipidus was present in 30% (16/53); 8 had abnormal hypothalamic–pituitary axis imaging. Radiographic evidence of CNS involvement (i.e., dural, brain, including Fazekas score >1, or spinal cord) occurred in 55% (22/40) and was unassociated with significantly increased mortality. Conclusions Erdheim-Chester disease commonly and variably involves the neuraxis. Patients with suspected Erdheim-Chester disease should undergo MRI brain and spine and screening investigations (serum sodium, serum and urine osmolality) for diabetes insipidus to clarify extent of neurologic disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".