Atopy and Risk of Brain Tumors: A Meta-analysis
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| 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) | 0.000 | 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 it