Erdheim-Chester disease with bone scan, FDG PET, MRI, and CT findings
Bibliographic record
Abstract
1337 Learning Objectives 1.Present a case of Erdheim-Chester Disease (ECD) in a 59 year old female with initial presentation of diabetes insipidus of uncertain etiology and non-contributive pituitary imaging 2.Describe the patient’s bone scan, FDG PET/CT, CT and MRI findings and review typical imaging findings 3. Review the epidemiology, pathology, clinical presentation and treatment of ECD Erdheim-Chester disease (ECD) is a rare, non-Langerhans histiocytic disorder with fewer than 500 cases described in the literature. The most common manifestation is polyostotic sclerotic lesions with the majority of cases also demonstrating soft-tissue involvement typically affecting the maxillary sinus, large vessels, heart, lungs, CNS and retroperitoneum. We present the case of a 59 year old female initially referred by her dentist for further evaluation of jaw pain and orthopantographic findings of a mandibular lesion. CT scan of the facial bones showed multiple lucencies throughout the mandibles, suggestive of a chronic process. Bone scan identified bilateral and symmetric lesions in the lower and upper extremities and mandibles and focal lesions in the cervical spine, left rib cage, and the pelvis, suspicious for ECD. MRI of the axial skeleton showed multiple bilateral and symmetric sclerotic foci in the proximal humeri, femurs, iliac bones, cervical and lumbar spine. The patient also had a FDG PET/CT scan which showed a similar distribution of lesions as the bone scan. The diagnosis of ECD was confirmed by biopsy.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".