Multiplex serum cytokine monitoring as a prognostic tool in rheumatoid arthritis.
Bibliographic record
Abstract
OBJECTIVE: Early optimized therapy of rheumatoid arthritis (RA) results in improved outcomes. The initiation of optimized therapy is hindered by the difficulty of early diagnosis and the limitations of current disease activity and therapeutic response assessment tools. Identifying patients requiring early combination DMARD/biologic therapy is currently a significant clinical challenge given the lack of definitive prognostic criteria. Since cytokines are soluble intracellular signaling molecules that modulate disease pathology in RA, we tested the recent conjecture that en mass serum cyto-kine measurement and monitoring will provide a useful tool for effective therapeutic management in RA. METHODS: We assayed the levels of 16 serum cytokines in 18 RA patients treated prospectively with methotrexate and from 18 unaffected controls. Specific mechanistic aspects of inflammatory pathology in the periphery could be discerned on a patient-specific basis from patients' serum cytokine profiles, information that may aid in the design of anti-cytokine biologic therapy. A serum Cytokine Activity Index (CAI) was also created using multi-variant analysis methods. RESULTS: Distinct cytokines were significantly elevated in RA patients relative to controls, and three distinct clusters with correlations to disease activity were identified. The Cytokine Activity Index correlated well with the therapeutic res-ponse; responders and non-responders in this cohort were distinguishable as early as one month post initiation of methotrexate therapy, well before clinical assessments of response are commonly completed. CONCLUSION: Clinical assessment tools could be derived from this approach that may provide a means to continually track patients, allowing intervention strategies to be better evaluated on a patient-specific basis and to identify residual cytokine activity that could be used to guide combination therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".