Idiopathic Pulmonary Fibrosis and Lung Cancer. A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Rationale The association between idiopathic pulmonary fibrosis (IPF) and lung cancer has been previously reported. However, there is the potential for significant confounding by age and smoking, and an accurate summary risk estimate has not been previously ascertained. Objectives To determine the risk and burden of lung cancer in patients with IPF, accounting for known confounders. Methods We conducted a comprehensive literature search of MEDLINE, EMBASE, and SCOPUS databases and used the Newcastle Ottawa criteria to assess study quality. We then assessed the quality of ascertainment of IPF cases based on modern consensus criteria. Data that relied on administrative claims or autopsies were excluded. We calculated summary risk estimates using a random effects model. Results Twenty-five cohort studies were included in the final analysis. The estimated adjusted incidence rate ratio from two studies was 6.42 (95% confidence interval [CI], 3.21–9.62) and accounted for age, sex, and smoking. The summary incidence rate from 11 studies was 2.07 per 100 person-years (95% CI, 1.46–2.67), and the summary mortality rate was 1.06 per 100 person-years (95% CI, 0.62–1.51) obtained from three studies. The summary prevalence from 11 studies was 13.74% (95% CI, 10.17–17.30), and the proportion of deaths attributable to lung cancer was 10.20 (95% CI, 8.52–11.87) and was obtained from nine studies. Conclusions IPF is an increased independent risk factor for lung cancer, even after accounting for smoking. Further well-designed studies using modern consensus criteria are needed to explore mechanisms of this association.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.010 | 0.010 |
| 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".