Neurology (Neuromuscular)
Bibliographic record
Abstract
Background: IL-27 acts as a ‘master regulator’ in modulating inflammation and was responsible for a number of autoimmune diseases. However, the role of IL-27 was not addressed in Guillain-Barré syndrome (GBS). Methods: Sixty-five subjects including 19 with GBS, 7 with encephalitis or meningitis, 23 with multiple sclerosis or neuromyelitis optica as well as 11 with other non-inflammatory neurological disorders were enrolled. ELISA was used to detect the concentrations of IL-27 in paired samples of cerebrospinal fluid and plasma. Results: The mean concentration of IL-27 in GBS patients was significantly lower than in other neurological disorders both in CSF and in plasma (all p<0.05). GBS patients with cranial involvement, decreased reflexes, hypaesthesia, autonomic nerve dysfunction, MRC score <30 are inclined to have a lower CSF IL-27 level than patients without these symptoms (182 pg/ml, 181 pg/ml, 185 pg/ml, 185 pg/ml, 194 pg/ml vs 211 pg/ml, 205 pg/ml, 202 pg/ml, 198 pg/ml, 199 pg/ml, respectively). Similar results were noted in plasma except for cranial involvement. Conclusions: Production of IL-27 was disparate between GBS and other neurological diseases and a significantly lower level of IL-27 was observed in GBS patients, indicative of an anti-inflammatory role of IL-27 in GBS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.232 | 0.111 |
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".