Relationship between reduced forced expiratory volume in one second and the risk of lung cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Individuals with severely impaired lung function have an increased risk of lung cancer. Whether milder reductions in forced expiratory volume in 1 second (FEV(1)) also increase the risk of lung cancer is controversial. Moreover, there is little consensus on whether men and women have similar risks for lung cancer for similar decreases in FEV(1). METHODS: A search was conducted of PubMed and EMBASE from January 1966 to January 2005 and studies that examined the relationship between FEV1 and lung cancer were identified. The search was limited to studies that were population based, employed a prospective design, were large in size (> or = 5000 participants), and adjusted for cigarette smoking status. RESULTS: Twenty eight abstracts were identified, six of which did not report FEV1 and eight did not adjust for smoking. Included in this report are four studies that reported FEV1 in quintiles. The risk of lung cancer increased with decreasing FEV1. Compared with the highest quintile of FEV1 (> 100% of predicted), the lowest quintile of FEV1 (< approximately 70% of predicted) was associated with a 2.23 fold (95% confidence interval (CI) 1.73 to 2.86) increase in the risk for lung cancer in men and a 3.97 fold increase in women (95% CI 1.93 to 8.25). Even relatively small decrements in FEV1 ( approximately 90% of predicted) increased the risk for lung cancer by 30% in men (95% CI 1.05 to 1.62) and 2.64 fold in women (95% CI 1.30 to 5.31). CONCLUSION: Reduced FEV1 is strongly associated with lung cancer. Even a relatively modest reduction in FEV1 is a significant predictor of lung cancer, especially among women.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".