Maternal medication use and the risk of brain tumors in the offspring: The SEARCH international case‐control study
Bibliographic record
Abstract
N-nitroso compounds (NOC) have been associated with carcinogenesis in a wide range of species, including humans. There is strong experimental data showing that nitrosamides (R(1)NNO.COR(2)), a type of NOC, are potent neuro-carcinogens when administered transplacentally. Some medications are a concentrated source of amides or amines, which in the presence of nitrites under normal acidic conditions of the stomach can form NOC. Therefore, these compounds, when ingested by women during pregnancy, may be important risk factors for tumors of the central nervous system in the offspring. The aim of the present study was to test the association between maternal use of medications that contain nitrosatable amines or amides and risk of primary childhood brain tumors (CBT). A case-control study was conducted, which included 1,218 cases and 2,223 population controls, recruited from 9 centers across North America, Europe and Australia. Analysis was conducted for all participants combined, by tumor type (astroglial, primitive neuroectodermal tumors and other glioma), and by age at diagnosis (< or =5 years; >5 years). There were no significant associations between maternal intake of medication containing nitrosatable amines or amides and CBT, for all participants combined and after stratification by age at diagnosis and histological subtype. This is the largest case-control study of CBT and maternal medications to date. Our data provide little support for an association between maternal use of medications that may form NOC and subsequent development of CBT in the offspring.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".