Diagnostic value of soluble Interleukin-2 receptor in patients suffering neurosarcoidosis: A systematic review
Bibliographic record
Abstract
Background: Neurosarcoidosis is an inflammatory granulomatous disease. Up to 25% of occult sarcoidosis affecting the nervous system are only detected by autopsy. In addition, in recent years the suspicion arose that the soluble Interleukin-2 Receptor (sIL-2R) might be useful in differentiating between neurosarcoidosis and neurosarcoidosis-like diseases such as neurotuberculosis, multiple sclerosis, or cerebral lymphoma. Objectives: Therefore, we aimed to systematically review randomized controlled trials (RCT), observational studies, and case-control studies evaluating sIL-2R levels in neurosarcoidosis patients. Design: 2024. Data Sources and Methods: As part of the search strategy conducted, 6 articles met the inclusion criteria. Two independent reviewers extracted the relevant data from each article. In addition, 2 independent reviewers assessed the quality of each study using the Newcastle-Ottawa Scale (NOS). Results: We included 6 studies comprising 98 patients suffering from neurosarcoidosis, 525 non-sarcoidosis patients, and 118 healthy controls. Included studies were published between 2010 and 2023. Cerebrospinal fluid (CSF) sIL-2R levels differed significantly between neurosarcoidosis patients and multiple sclerosis, vasculitis, and healthy controls whereas serum sIL-2R levels did not reveal sufficient discriminative power. sIL-2R index was able to discriminate neurosarcoidosis from neurotuberculosis, bacterial/viral meningitis, and healthy controls. Conclusions: In this systematic review, we found indications that sIL-2R may be a useful biomarker for the diagnosis of neurosarcoidosis. To determine an additional diagnostic value of sIL-2R, large prospective studies are needed that not only examine absolute sIL-2R levels in serum or CSF but also the dynamic changes as well as the implications of renal function on sIL-2R levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".