Cancer risk in children and young adults born preterm: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Risk of developing a malignancy when born premature is unknown. We hypothesised that risk of certain cancers might be increased in youth born preterm versus term. We therefore performed a systematic review and meta-analysis to evaluate the incidence of malignancy in the context of preterm birth, according to various cancer types. METHODS: The study was designed per MOOSE and PRISMA guidelines. Articles were identified through November 2015. Observational studies exploring the association between childhood malignancy and birth characteristics were included. Of the 1658 records identified, 109 full text articles were evaluated for eligibility. Random effects meta-analyses were conducted on 10/26 studies retained; 95% confidence intervals were computed and adjusted following sensitivity analysis. Publication bias was evaluated using funnel plots, Begg's and Egger's tests. RESULTS: No differences in risk of primary central nervous system tumor [OR 1.05; 95% CI 0.93-1.17, 5 studies, 580 cases] and neuroblastoma [OR 1.09; 95% CI 0.90-1.32, 5 studies, 211 cases] were observed in individuals born <37 versus ≥37 weeks' gestation. Preterm birth was consistently associated with hepatoblastoma [ORs 3.12 (95% CI 2.32-4.20), 1.52 (95% CI 1.1-2.1), 1.82 (95% CI 1.01-3.26), and 2.65 (95% CI 1.98-3.55)], but not leukemia, astrocytoma, ependymoma, medulloblastoma, lymphoma, nephroblastoma, rhabdomyosarcoma, retinoblastoma or thyroid cancer. CONCLUSIONS: Children born premature may be at increased risk for hepatoblastoma but there is no strong evidence of an increased risk of primary central nervous system tumours or neuroblastoma. There is insufficient evidence to conclude whether prematurity modulates the risk of other childhood cancers.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 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".