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 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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".