Infliximab in neurosarcoidosis: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives To evaluate the clinical outcomes and relapse rates in neurosarcoidosis patients administered infliximab. Methods A systematic review was conducted using the MEDLINE, EMBASE, SCOPUS, and Cochrane Library databases. The search included studies from their inception to March 2023. We included case‐series studies with at least 10 neurosarcoidosis patients undergoing any treatment type. Studies were also required to report at least one of the following outcomes: response rate, overall survival rate, or relapse rate. This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. A random‐effects model facilitated the analysis of proportional treatment outcomes. Study quality was evaluated using the modified Newcastle–Ottawa quality assessment scale, while a funnel plot helped detect any publication bias. Results Seven studies comprising 237 patients with neurosarcoidosis were included in the analysis. Of these patients, 184 (77.6%) received treatment with infliximab. The pooled proportion of patients showing clinical improvement after infliximab treatment was 0.74 (95% CI 0.64–0.84, I 2 = 49.73%). Relapse rates, derived from four studies, stood at 0.38 (95% CI 0.22–0.55, I 2 = 56.92%). Most studies reported successful tapering or cessation of corticosteroid dosage in patients receiving infliximab. Adverse effects were reported in 52 (29.4%) patients, of which 39 out of 54 events (72.2%) were linked to infections. Interpretation Infliximab demonstrated potential improvement in clinical outcomes for patients with refractory neurosarcoidosis and showed potential for reducing the dosage of concurrent corticosteroids. However, a degree of relapse was observed, with infections being the primary concern for adverse events.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.042 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".