Birth characteristics and the risk of childhood brain tumors: A case–control study in Ontario, Canada
Bibliographic record
Abstract
Various birth characteristics may influence healthy childhood development, including the risk of developing childhood brain tumors (CBTs). In this study, we aimed to investigate the association between delivery methods, obstetric history, and birth anthropometrics with the risk of CBTs. This study used data from the Childhood Brain Tumour Epidemiology Study of Ontario (CBREO) which included children 0-15 years of age and newly diagnosed with CBTs from 1997 to 2003. Multivariable logistic regressions were performed to explore the association between delivery methods, obstetric history, and birth anthropometric variables, with subsequent CBT development. Models were adjusted for maternal and index child characteristics, and stratified by histology where sample size permitted. The use of assistive instruments (forceps or suction) during childbirth was significantly associated with overall CBTs (OR 1.84, 95% CI 1.30-2.61) and non-glial tumors (OR 2.57, 95% CI 1.60-4.13). Compared to first-born children, those second-born or greater had a lower risk of overall CBT development (OR 0.74, 95% CI 0.55-0.98), and glial histological subtype. All other birth characteristic variables explored were not associated with CBTs. The use of assistive devices such as forceps or suction during vaginal delivery carries potential risks, including increased risk of CBT development. There is an inverse association between birth order and CBTs, and future studies examining early childhood common infection may be warranted.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".