Bibliographic record
Abstract
Introduction: Lung cancer and Allergic Rhinitis (AR) have contradictory correlation; positive in some cases while negative association for some cancers. The incidence of both AR and lung cancer is high posing highest percentage of deaths. Some recent research has reported positive association between asthma and lung cancer, so our research questions this correlation.This systematic review and meta-analysis includes results from seven studies filtered through strict inclusion and exclusion criteria. Pooled analysis [OR:0.56;95% CI: 0.45-0.70; p-value <0.00001], and [RR:0.63; 95% CI:0.51-0.77; p-value<0.00001] exhibits a strong negative correlation between lung cancer and AR. Small cell lung cancer (SCLC) association was stronger than the non–small cell lung cancer (NSCLC) [RR:0.64,95%CI: 0.53-0.77], with a p-value<0.00001;although this was present in one study only. The study in Canada (OR: 0.35 and RR: 0.38) and in Germany (OR: 0.18 and RR: 0.19) had lower OR and RR values compared to the studies in the USA (OR 0.62 and RR 0.69). Two Canadian and one German study was an outlier; as sensitivity analysis reduces heterogeneity from 64% to 27% (adds ration) and 72% to 40% (risk ration) when analysis was conducted excluding these three studies.Conclusions: Current research is insufficient to determine whether there is correlation between AR and lung cancer. We recommend that new epidemiological studies should be conducted to establish this relationship clearly.Keywords: Allergic Rhinitis, Hay fever, Allergy, Cancer, Lung Cancer, Correlation
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.460 | 0.293 |
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".