The association between birth order and childhood brain tumors: a systematic review and meta-analysis
Bibliographic record
Abstract
The incidence of childhood brain tumors (CBT) has increased worldwide, likely resulting from the improvements of early diagnostics. We conducted a systematic review and meta-analysis to clarify the association between birth order and CBT. We followed established guidelines to systematically search Ovid Medline, PubMed, and the Cochrane Library for English language studies, published before March 2018. Quality assessment was performed using the Newcastle-Ottawa Scale. Meta-analysis provided pooled risk estimates and their 95% confidence intervals (CIs) for birth order and CBT. We identified 16 case-control studies with a total sample of 32 439 cases and 166 144 controls and three prospective cohort studies (i.e. 4515 incident cases of CBTs among 5 281 558 participants). Compared with first birth order, the meta-odds ratio for second birth order in case-control studies was 1.04 (95% CI: 1.01-1.07), that for third birth order was 0.98 (95% CI: 0.90-1.06), and that for fourth order was 0.85 (95% CI: 0.78-0.92). The meta-hazard ratio for second or higher birth order compared with first birth order in cohort studies was 1.00 (95% CI: 0.96-1.05). We found no association between birth order and CBT in both case-control and cohort study designs; the small association observed for fourth birth order deserves further consideration.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".