History of allergic diseases and risk of cancer
Bibliographic record
Abstract
Epidemiological studies focusing on allergic diseases in relation to cancer \nrisk have generated inconsistent results, suggesting beneficial, harmful, or \nno effects. We studied whether a history of asthma and eczema was associated with the risk of 7 cancer types among men. In the 1980s, we conducted a large population-based case-control study of environmental causes \nof cancer among males in Montreal, Canada, including several types of \ncancer. Information collected by interview included prior diagnosis of \nasthma, eczema and other medical conditions, age at diagnosis, and medication use. We compared the self-reported medical history from cases of \nstomach (n = 227), colon (n = 438), rectal (n = 236), lung (n = 755), \nprostate (n = 397), bladder (n = 438) cancer and non-Hodgkin’s lymphoma \n(n = 197), to that of population controls (N ¼ 512). Logistic regression \nmodels were used to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CI) for the associations between asthma and eczema, and \neach cancer type. Among population controls, the prevalences of asthma \nand eczema were 5.3% and 4.5%. Prior history of asthma was associated \nwith none of the cancers, but when restricting exposure to those who used \nmedication, asthma was negatively associated with stomach cancer: OR = \n0.27 [95% CI: 0.1–0.9]. Prior history of eczema was inversely associated \nwith all cancers, but only lung cancer achieved statistical significance: OR = \n0.34 [95% CI: 0.2–0.7]. It has been hypothesized that allergic conditions, \nresulting from a hyper-reactive immune system, might lead to a more efficient elimination of abnormal cells and thus lower cancer risks. Although \nlimited by small numbers, our results bring some support to this hypothesis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".