Many faces of neurosarcoidosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Neurosarcoidosis occurs in 5% of patients with sarcoidosis and can be difficult to diagnose. In this review we discuss the most recent advances in our understanding of the disease, describing clinical characteristics, diagnostic process, treatment, and prognosis. RECENT FINDINGS: Clinical presentation is heterogeneous with most patients presenting with cranial nerve palsy, headache, or sensory abnormalities. Patients are classified according to probability of the diagnosis with the Zajicek criteria. In these criteria, histopathological confirmation of noncaseating granulomas in affected tissue outside the nervous system is key. Radiological abnormalities on neuroimaging are nonspecific. No biomarkers have been described that adequately identify patients with sarcoidosis. However, soluble interleukin-2 receptor is a relatively novel biomarker that may be useful. In addition to HRCT scan, F-FDG PET-CT scanning can identify occult locations of disease activity and aid in obtaining pathological confirmation. Despite the use of new therapies, still a third of patients remains stable, deteriorate, or die. SUMMARY: Diagnosing and treating patients with neurosarcoidosis remains a challenge. Long-term prospective studies evaluating patients suspected of neurosarcoidosis are needed to assess sensitivity and specificity of ancillary investigations and diagnostic criteria. Furthermore, future studies are needed to evaluate the prognosis and the optimal treatment strategy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".