Correlations between changes in cytokines and clinical outcomes for early phase (proof of concept) trials in active diffuse systemic sclerosis using data from an imatinib study
Bibliographic record
Abstract
OBJECTIVE: Data from a small study testing imatinib to treat SSc were used to determine if cytokine changes were related to differences in clinical parameters to model future early phase trials pairing cytokine changes and clinical parameters. METHODS: Plasma and punch skin biopsy specimens collected at baseline and 6 months were analysed for levels of 26 fibrotic and inflammatory cytokines using multiplexed immunoassays and ELISA. Seven of nine patients on active treatment had paired data. Biopsies were biopulverized and standardized to protein levels in the tissue homogenate. Plasma was frozen at -80°C and analysed using multiplexed immunoassays or ELISAs standardized to CRP. Correlations between fold changes in cytokines and differences in clinical parameters (skin score, physician and patient global assessments and HAQ) were performed. P < 0.01 was considered significant. RESULTS: After 6 months of imatinib treatment, plasma levels of soluble vascular cell adhesion molecule 1 decreased significantly (P < 0.001), while tissue levels of soluble intercellular adhesion molecule 1 increased (P < 0.01). Some significant correlations between fold changes in certain plasma fibrotic and inflammatory cytokines and changes in clinical parameters after 6 months of treatment were found: patient global scores and IL-13 (r = 0.964, P < 0.0001); ESR and IL-12p70 (r = -0.903, P < 0.01); in tissue samples, patient global score and soluble E-selectin (r = 0.913, P < 0.01); and physician global score with sCD40L (r = -0.883, P < 0.01). CONCLUSION: Some serum and tissue cytokines may have a role in early phase clinical trials of SSc, correlating with changes in clinical parameters. Serum and tissue samples could be analysed in early phase trials to determine whether they support the clinical observations. TRIAL REGISTRATION: http://clinicaltrials.gov/show/NCT01545427.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".