Association between cancer and allergies
Bibliographic record
Abstract
BACKGROUND: The prevalence of allergies and the incidence of cancer are both increasing worldwide. It has been hypothesized that atopy may affect the risk of some cancers. METHODS: In this study, 1525 patients (754 women and 771 men with a mean age of 52.7 ± 11.9 years) with different types of cancer were examined for the presence of allergies. Allergies were confirmed based on retrospective analysis of allergy diagnostic procedures in patients previously diagnosed with cancer. All patients were also analyzed for bronchial asthma and allergic rhinitis according to relevant guidelines. A control group of patients without cancer diagnoses was used for comparison. RESULTS: Patients with cancer had significantly fewer IgE-mediated allergic diseases than the control population. For the oncological group compared to the non-cancer patients, the odds ratios (ORs) for allergic rhinitis, atopic dermatitis, and bronchial asthma were 0.67 (95 % CI 0.52-0.81), 0.89 (95 % CI 0.78-0.99), and 1.03 (95 % CI 0.91-1.13), respectively. The mean serum concentrations of total IgE were significantly lower in the study population of patients with cancer than in the patients in the control group (45.98 ± 14.9 vs. 83.2 ± 40.1 IU/l; p < 0.05). There were no significant correlations between the type of cancer diagnosed and the form of allergy. CONCLUSION: Our results indicate that the overall incidence of allergies, particularly allergic rhinitis, was lower in patients with some types of cancer. Further studies are needed to confirm our findings.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".