Reproductive Health Outcomes among Adolescent and Young Adult Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: This systematic review and meta-analysis (SRMA) aimed to synthesize the current literature on the impacts of adolescent and young adult (AYA, ages 15-39 years) cancer on reproductive health outcomes. METHODS: EMBASE and Medline were searched from 1 January 2000 to 26 January 2022 for observational studies that included individuals with AYA cancer and controls which evaluated reproductive health outcomes. We used random effects models and 95% confidence intervals to obtain pooled measures of associations between AYA cancer, cancer treatment, and reproductive health outcomes. RESULTS: The search identified 8625 articles; 21 were included. 62 reproductive outcomes were assessed and classified according to a sex-based framework as fetal/neonatal (n = 26), maternal (n = 11), fetal/neonatal-maternal (n = 23), and maternal-paternal (n = 2). Meta-analyses of crude estimates showed significant associations between AYA cancer and outcomes including preterm birth (pooled odds ratio [pOR] 1.31; 95% CI: 1.22, 1.42), gestational diabetes (pOR 1.43; 95% CI: 1.03, 1.99), and fertility treatment (pOR 2.66; 95% CI 1.71, 4.11). We also found higher odds of preterm birth (pOR 1.65; 95% CI: 1.21, 2.26) and low APGAR score at birth (pOR 2.03; 95% CI: 1.32, 3.13) among AYA cancer patients who received radiation compared to controls. CONCLUSIONS: Our SRMA quantified impacts of AYA cancers and treatments on several reproductive health outcomes.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.042 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".