215 Active arthritis is associated with 14–3–beta titre in patients with systemic lupus erythematosus
Bibliographic record
Abstract
Background and aims Non-erosive arthritis is common in systemic lupus erythematosus (SLE). 14-3-3 eta, a chaperone protein that activates pro-inflammatory pathways is emerging as a novel biomarker for erosive Rheumatoid Arthritis. We investigated clinical associations of serum 14-3-3 eta in SLE focusing on arthritis. Methods Sociodemographics, ACR criteria, and SLEDAI were recorded. Arthritis, assessed by the SLEDAI, was categorised as active (n=78), inactive (n=138) and never present (n=49). Serum 14-3-3 eta was measured by ELISA; titres above 0.19 ng/ml were considered positive. We report descriptive statistics and logistic regression models testing the association of 14-3-3eta with arthritis state. Results SLE patients (n=265) were mainly female (92%), Caucasian (67%) with a mean (SD) age of 51.7 (14) years, and median (25%,75%) disease duration of 8 (4,10) years, number ACR criteria of 6 (5,7), and SLEDAI of 4 (2,7). 241 (81%) had active or inactive arthritis. 14-3-3 eta positivity was similar across the three arthritis groups (active 22/78 (28%), inactive 27/138 (20%), never present 10/49 (20%) with a median (25%75%) titre of 0.6 ng/ml (0.34, 1.82). The highest quartile of 14-3-3eta associated with active arthritis (OR 3.6 (95% CI 1.33, 9.98) p-0.012) after adjusting for ethnicity and SLEDAI. There were no differences in 14-3-3 eta positivity for other lupus criteria nor correlation of 14-3-3 eta titer with number of ACR criteria or SLEDAI. Conclusions 14-3-3 eta titers are highest in lupus patients with active arthritis suggesting a higher risk for more severe arthritis. Further work will explore the associations of 14-3-3 eta in lupus with erosive arthritis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".