Smoking and Idiopathic Pulmonary Fibrosis: A Meta-analysis
Bibliographic record
Abstract
INTRODUCTION: In this study, we aimed to systematically explore the relationship between smoking and idiopathic pulmonary fibrosis (IPF). AIMS AND METHODS: The PubMed, Web of Science, and Embase databases were searched to systematically identify eligible studies. The Newcastle‒Ottawa Quality Assessment Scale (NOS) was used to evaluate the quality of the selected studies. The pooled odds ratio (OR) and survival hazard ratio (HR) were calculated with a random effects model using Stata 16.0 software. RESULTS: Thirty studies were enrolled. All of the included studies were considered to have intermediate or high quality. Nine studies were suitable for meta-analysis of ORs, and 21 studies were suitable for meta-analysis of survival HR. The pooled analysis revealed a significant difference in the risk of IPF between the smoking group and the never-smoking group (OR 1.71, 95% CI: 1.27 to 2.30, p < .001), indicating that smoking is a risk factor for IPF. When analyzing pooled survival HRs, never smoking was compared to former smoking or current smoking. Former smoking was shown to be a poor prognostic factor for IPF (HR 1.43, 95% CI: 1.18 to 1.74, p < .001), but current smoking was not a significant factor. CONCLUSIONS: Our results indicated that smoking is a risk factor for IPF patients. IMPLICATIONS: In this study, we mainly concluded that smoking is a risk factor for IPF and that former smoking is a poor prognostic factor for IPF. To the best of our knowledge, this is the first meta-analysis report focusing on the association between smoking per se and IPF. Through our current study, we hope to further raise awareness of the relationship between smoking and IPF.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.048 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".