Identification of clinically relevant subgroups of COPD based on airway and circulating autoantibody profiles
Bibliographic record
Abstract
Autoimmunity may serve a role in the pathological features of a subgroup of patients with chronic obstructive pulmonary disease (COPD); however, in immunological subgroups of COPD patients, the interrelationships between airway and circulating autoantibody responses, and clinical parameters, remain unclear. The present study was undertaken to evaluate these interrelationships in various immunological subgroups of COPD patients. Sputum supernatant and serum obtained from 102 patients with stable COPD were assayed for the presence of immunoglobulin G antibodies against ten autoantigens via Luminex multiplex technology. Hierarchical clustering based on principal components was performed on autoantibody profiles to classify patients into clusters. Network‑based and module analyses were conducted to explore interrelationships among autoantibodies and clinical variables in each cluster. Topological characteristics were compared between clusters. Unsupervised clustering identified four clusters: No significant differences in the majority of clinical characteristics were observed among clusters. In cluster 1, retrospective exacerbation was only positively associated with COPD assessment test score. Lung functions (predicted % of forced expiratory volume in 1 sec and maximal mid‑expiratory flow) were negatively associated with exacerbation risk only in cluster 2. Sputum autoantibodies (against U1 small nuclear ribonucleoprotein, proteinase‑3 and Ro/Sjögren syndrome type A antigen) were negatively associated with exacerbation risks in cluster 2, but positively associated in cluster 3. The four networks also exhibited distinct topological properties. In COPD, autoantibody responses were heterogeneous and differentially associated with exacerbation risk in certain subgroups; their dual character should be considered in future research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".