Atopy and Risk of Brain Tumors: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Glioma is a rapidly progressive disease, and little is known about its etiology. Atopic diseases are on the rise in western populations, with increasing interest on their long-term health consequences. An inverse association between atopy and the risk of glioma has been observed. We carried out a meta-analysis of studies examining the association between atopic disease and risk of glioma and meningioma. METHODS: In an electronic literature search of the MEDLINE, ISI Web of Science, and EMBASE databases from 1979 through February 2007, we identified case-control and cohort studies quantifying associations between a history of asthma, eczema, or hay fever or allergy and a medically confirmed diagnosis of glioma or meningioma. We performed meta-analysis by pooling studies according to the inverse of their variances. We evaluated publication bias using funnel plot and sensitivity analyses. RESULTS: A total of eight observational studies were included, with a total of 3450 patients diagnosed with glioma and 1070 patients with meningioma. A history of atopic disease was inversely related to risk of glioma. The pooled relative risks (RRs) of glioma comparing those with a history of an atopic condition with those with no history of that condition were 0.61 (95% confidence interval [CI] = 0.55 to 0.67) for allergy, 0.68 (95% CI = 0.58 to 0.80) for asthma, and 0.69 (95% CI = 0.58 to 0.82) for eczema. Proxy reporting was unlikely to explain the association because the pooled relative risk estimate from studies without proxy reporting remained inverse and statistically significant (RR = 0.66, 95% CI = 0.58 to 0.75). Publication bias was also an unlikely explanation for the inverse association because the association persisted in a sensitivity analysis and the funnel plot was symmetric. No overall statistically significant association was noted for atopy and meningioma, although the information on this disease was limited and heterogeneous. CONCLUSIONS: We observed a strong inverse relationship between atopic disease and glioma that is unlikely to be explained by methodologic bias alone.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.052 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".