Disseminated Cryptococcal Infection and Anti-Tumor Necrosis Factor-α Treatment for Refractory Sarcoidosis: An Expected Association?
Bibliographic record
Abstract
The use of anti-tumor necrosis factor-α(anti-TNF-α) agents is validated in refractory rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, Crohn’s disease, and ulcerative colitis. Although very effective in breaking down granulomatous inflammation typically involved in sarcoidosis, anti-TNF-α agents significantly reduce the host granulomatous defence mechanisms that normally contain pathogens such as mycobacteria and fungi. We describe a case of disseminated cryptococcosis in a patient with refractory systemic sarcoidosis, in whom complete resolution followed discontinuation of anti-TNF-α drug and antifungal therapy. A 42-year-old man was referred to our internal medicine department with a 2-year history of unexplained left-ear deafness and lymphocytic meningitis. Clinical examination was normal except for left-ear deafness. Gadolinium-enhanced magnetic resonance imaging (MRI) showed multiple solid enhancing lesions involving the left internal acoustic canal and the left frontal lobe. Cerebrospinal fluid (CSF) analysis revealed lymphocytosis (85/mm3), elevated CSF protein (1.24 g/l), and low glucose CSF concentrations (1.4 mmol/l). Viral, bacterial, and fungal cultures were negative. Thoracic computed tomography … Address reprint requests to Dr Sène; E-mail: damien.sene{at}psl.aphp.fr
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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 | 0.001 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".