Allergies and Risk of Pancreatic Cancer: A Pooled Analysis From the Pancreatic Cancer Case-Control Consortium
Bibliographic record
Abstract
In order to quantify the risk of pancreatic cancer associated with history of any allergy and specific allergies, to investigate differences in the association with risk according to age, gender, smoking status, or body mass index, and to study the influence of age at onset, we pooled data from 10 case-control studies. In total, there were 3,567 cases and 9,145 controls. Study-specific odds ratios and 95% confidence intervals were calculated by using unconditional logistic regression adjusted for age, gender, smoking status, and body mass index. Between-study heterogeneity was assessed by using the Cochran Q statistic. Study-specific odds ratios were pooled by using a random-effects model. The odds ratio for any allergy was 0.79 (95% confidence interval (CI): 0.62, 1.00) with heterogeneity among studies (P < 0.001). Heterogeneity was attributable to one study; with that study excluded, the pooled odds ratio was 0.73 (95% CI: 0.64, 0.84) (Pheterogeneity = 0.23). Hay fever (odds ratio = 0.74, 95% CI: 0.56, 0.96) and allergy to animals (odds ratio = 0.62, 95% CI: 0.41, 0.94) were related to lower risk, while there was no statistically significant association with other allergies or asthma. There were no major differences among subgroups defined by age, gender, smoking status, or body mass index. Older age at onset of allergies was slightly more protective than earlier age.
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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 | 0.025 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".