Circulating cytokine levels in systemic sclerosis related interstitial lung disease and idiopathic pulmonary fibrosis
Bibliographic record
Abstract
Exploration of cytokine levels in systemic sclerosis-associated interstitial lung disease (SSc-ILD) and idiopathic pulmonary fibrosis (IPF) is needed to find common and diverse biomolecular pathways. Circulating levels of 87 cytokines were compared amongst 19 healthy controls and consecutive patients with SSc-ILD (n = 39), SSc without ILD (n = 29), and IPF (n = 17) recruited from a Canadian centre using a log-linear model adjusted for age, sex, baseline forced vital capacity (FVC), and immunosuppressive or anti-fibrotic treatment at time of sampling. Also examined was annualized change in FVC. Four cytokines had Holm's corrected p-values less than 0.05. Eotaxin-1 levels were increased approximately two-fold in all patient categories compared to healthy controls. Interleukin-6 levels were eight-fold higher in all ILD categories compared to healthy controls. MIG/CXCL9 levels increased two-fold more in all but one patient category compared to healthy controls. Levels of a disintegrin and metalloproteinase with thrombospondin type 1 motif, member 13, (ADAMTS13) were lower for all categories of patients compared to controls. No substantial association was found for any of the cytokines with FVC change. Observed cytokine differences suggest both common and diverse pathways leading to pulmonary fibrosis. Further studies evaluating longitudinal change of these molecules would be informative.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".