Asthma and lung cancer risk: a systematic investigation by the International Lung Cancer Consortium
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Asthma has been hypothesized to be associated with lung cancer (LC) risk. We conducted a pooled analysis of 16 studies in the International Lung Cancer Consortium (ILCCO) to quantitatively assess this association and compared the results with 36 previously published studies. In total, information from 585 444 individuals was used. Study-specific measures were combined using random effects models. A meta-regression and subgroup meta-analyses were performed to identify sources of heterogeneity. The overall LC relative risk (RR) associated with asthma was 1.28 [95% confidence intervals (CIs) = 1.16-1.41] but with large heterogeneity (I(2) = 73%, P < 0.001) between studies. Among ILCCO studies, an increased risk was found for squamous cell (RR = 1.69, 95%, CI = 1.26-2.26) and for small-cell carcinoma (RR = 1.71, 95% CI = 0.99-2.95) but was weaker for adenocarcinoma (RR = 1.09, 95% CI = 0.88-1.36). The increased LC risk was strongest in the 2 years after asthma diagnosis (RR = 2.13, 95% CI = 1.09-4.17) but subjects diagnosed with asthma over 10 years prior had no or little increased LC risk (RR = 1.10, 95% CI = 0.94-1.30). Because the increased incidence of LC was chiefly observed in small cell and squamous cell lung carcinomas, primarily within 2 years of asthma diagnosis and because the association was weak among never smokers, we conclude that the association may not reflect a causal effect of asthma on the risk of LC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it