Serum 14-3-3η is a Novel Marker that Complements Current Serological Measurements to Enhance Detection of Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Serum 14-3-3η is a novel joint-derived proinflammatory mediator implicated in the pathogenesis of rheumatoid arthritis (RA). In our study, we assessed the diagnostic utility of 14-3-3η and its association with standard clinical and serological measures. METHODS: A quantitative ELISA was used to assess 14-3-3η levels. Early (n=99) and established patients with RA (n=135) were compared to all controls (n=385), including healthy subjects (n=189). The sensitivity, specificity, positive and negative predictive values of 14-3-3η, and the likelihood ratios (LR) for RA were determined through receiver-operator curve analysis. The incremental value of adding 14-3-3η to anticitrullinated protein antibody (ACPA) and rheumatoid factor (RF) in diagnosing early and established RA was assessed. RESULTS: Serum 14-3-3η differentiated established patients with RA from healthy individuals and all controls (p<0.0001). A serum 14-3-3η cutoff of ≥0.19 ng/ml delivered a sensitivity and specificity of 77% and 93%, respectively, with corresponding LR positivity of 10.4. At this cutoff in early RA, 64% of patients with early RA were positive for 14-3-3η, with a corresponding specificity of 93% (LR+ of 8.6), while 59% and 57% were positive for ACPA or RF, respectively. When ACPA, RF, and 14-3-3η positivity were used in combination, 77 of the 99 patients (78%) with early RA were positive for any 1 of the 3 markers. Serum 14-3-3η did not correlate with C-reactive protein, erythrocyte sedimentation rate, or Disease Activity Score, but patients who were 14-3-3η-positive had significantly worse disease. CONCLUSION: Serum 14-3-3η is a novel RA mechanistic marker that is highly specific, associated with worse disease, and complements current markers, enabling a more accurate diagnosis of RA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".