Interleukin 6 Levels and Disease Activity in Takayasu Arteritis
Bibliographic record
Abstract
BACKGROUND: Various studies have suggested interleukin 6 (IL-6) as a potential biomarker for detecting disease activity in Takayasu arteritis. METHODS: A systematic review and meta-analysis was performed to assess differences in IL-6 levels in patients with active (aTA) and inactive Takayasu arteritis (iTA), as well as healthy controls (HCs), using validated activity scores. Study quality and the risk of bias were assessed using STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and the Newcastle-Ottawa and Joanna Briggs checklist, respectively. For the meta-analysis, we pooled the raw mean IL-6 levels in each group and then estimated and pooled the crude mean differences between the groups. We applied a random-effects model in all analyses. RESULTS: Of the 93 eligible articles, 10 were included after removing duplicates and studies that met the exclusion criteria. Overall, 1825 patients with a mean age ranging from 24 to 40.6 years were included. The pooled levels of IL-6 increased depending on disease activity as follows: HCs: 3.08 (95% confidence interval [CI], 0.88-5.28), iTA: 7.21 (3.61-10.82), and aTA: 22.67 (12.44-32.91) pg/mL. Patients with aTA had higher IL-6 levels than HCs (21.52 [95% CI, -0.43 to 43.47]) and those with iTA (16.69 [95% CI, 5.32-28.06]), whereas IL-6 levels were not different between HCs and patients with iTA (3.62 [95% CI, -13.18 to 20.42]). CONCLUSIONS: Interleukin 6 levels are significantly increased in patients with aTA compared with those with iTA and HCs but not in patients with iTA compared with HCs. More studies are needed to establish the IL-6 cutoff value for assessing disease activity.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".