Cancer Mortality among US Men and Women with Asthma and Hay Fever
Bibliographic record
Abstract
The relation between self-reported physician-diagnosed asthma and/or hay fever and cancer mortality was explored in a prospective cohort study of 1,102,247 US men and women who were cancer-free at baseline. During 18 years of follow-up, from 1982 to 2000, there were 81,114 cancer deaths. Cox proportional hazards models were used to obtain adjusted relative risks for all cancer mortality and for cancer mortality at 12 sites associated with allergy indicators. There were significant inverse associations between a history of both asthma and hay fever and overall cancer mortality (relative risk (RR) = 0.88, 95% confidence interval (CI): 0.83, 0.93) and colorectal cancer mortality (RR = 0.76, 95% CI: 0.64, 0.91) in comparison with persons with neither of these allergic conditions. A history of hay fever only was associated with a significantly lowered risk of pancreatic cancer mortality, and a history of asthma only was associated with a significantly lowered risk of leukemia mortality. In never smokers, these associations persisted but were no longer significant. Results for mortality from cancer at other sites were less consistent. Collectively, these results suggest an inverse association between a history of allergy and cancer mortality; however, the strength of evidence for this association is limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".