Birth Anomalies and Obstetric History as Risks for Childhood Tumors of the Central Nervous System
Bibliographic record
Abstract
OBJECTIVE: The causes of childhood central nervous system (CNS) tumors are largely unknown. Birth characteristics have been examined as possible risk factors for childhood CNS tumors, although the studies have been underpowered and inconclusive. We hypothesized that birth anomalies and a mother's history of previous pregnancy losses, as a proxy for genetic defects, increase the risk for CNS tumors. METHODS: From the California Cancer Registry, we identified 3733 patients aged 0 to 14 years with CNS tumors, diagnosed from 1988 through 2006 and linked to a California birth certificate. Four controls were matched to each patient. We calculated odds ratios (ORs) for the reported presence of a birth defect and for history of pregnancy losses by using logistic regression, adjusted for race, Hispanic ethnicity, maternal age, birth weight, and birth order. RESULTS: Offspring from mothers who had ≥ 2 fetal losses after 20 weeks' gestation had a threefold risk for CNS tumors (OR: 3.13 [95% confidence interval (CI): 1.32-7.41]) and a 14-fold risk for high-grade glioma (OR: 14.28 [95% CI: 1.56-130.65]). Birth defects increased risk for the CNS cancers medulloblastoma (OR: 1.70 [95% CI: 1.12-2.57]), primitive neuroectodermal tumor (OR: 3.64 [95% CI: 1.54-8.56]), and germ cell tumors (OR: 6.40 [95% CI: 2.09-19.56]). CONCLUSIONS: Multiple pregnancy losses after 20 weeks' gestation and birth defects increase the risk of a childhood CNS tumor. Previous pregnancy losses and birth defects may be surrogate markers for gene defects in developmental pathways that lead to CNS tumorigenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".