Polymorphisms of interleukin-10 and its receptor and lung function in COPD
Bibliographic record
Abstract
Interleukin (IL)-10 is a type-2 T-helper cell cytokine with a broad spectrum of anti-inflammatory actions. Inflammation plays an important role in the pathogenesis of chronic obstructive pulmonary disease. It was hypothesised that single nucleotide polymorphisms (SNPs) of the genes encoding IL-10 (IL10) and the alpha subunit of its receptor (IL10RA) are associated with changes in, or value of, forced expiratory volume in one second (FEV1) in smoking-induced chronic obstructive pulmonary disease. In total, eleven SNPs of IL10 and IL10RA were studied in 586 White subjects, selected from continuous smokers followed for 5 yrs in the Lung Health Study, who showed the fastest (n=280) and slowest (n=306) decline in FEV1. These 11 SNPs were also studied in 1,072 participants exhibiting the lowest (n=538) and highest (n=534) baseline FEV1 at the beginning of the Lung Health Study. No association was found in the primary analyses. Although a subgroup analysis showed that the IL-10 3368A allele was associated with a fast decline in FEV1, the association did not pass correction for multiple comparisons. No gene-gene interaction of IL10 with IL10RA was found. There was no association of polymorphisms of the genes encoding interleukin-10 and the alpha subunit of its receptor with the rate of decline in, or value of, forced expiratory volume in one second in smoking-induced chronic obstructive pulmonary disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".