Tumour necrosis factor and lymphotoxin A polymorphisms and lung function in smokers
Bibliographic record
Abstract
Genetic variants in the tumour necrosis factor (TNF) gene have been investigated in chronic obstructive pulmonary disease (COPD). However, there are many instances of nonreplication of these associations due to insufficient power or other factors. In this study, a large number of subjects were examined to elucidate whether genetic variations of TNF and/or lymphotoxin A (LTA), which is clustered with TNF, are associated with variations in lung function among smokers. The present authors designed two nested case-control studies in the National Heart, Lung, and Blood Institute Lung Health Study (LHS), which enrolled 5,887 smokers. The first design included continuous smokers who had the fastest (n = 279) and the slowest (n = 304) decline of lung function during the 5-yr follow-up period, and the second included the subjects who had the lowest (n = 533) and the highest (n = 532) post-bronchodilator % predicted forced expiratory volume in one second at the start of the LHS. Within the TNF and LTA region, 10 tagging single-nucleotide polymorphisms were selected and genotyped. Unlike the previous associations between TNF-308 and COPD in Asians, the current study found no association between either of the two phenotypes and the LTA and TNF polymorphisms. In conclusion, these results support the findings of previous studies in late-onset chronic obstructive pulmonary disease in Caucasian populations.
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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 | 0.000 |
| 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".