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Record W4406858876 · doi:10.2196/65820

Assisted Reproductive Technology and Risk of Childhood Cancer Among the Offspring of Parents With Infertility: Systematic Review and Meta-Analysis

2025· review· en· W4406858876 on OpenAlexvenueaboutno aff
Song Gao, Cai-Qiong Zhang, Zhong-Ping Bai, Meng-qun Cheng

Bibliographic record

VenueJMIR Cancer · 2025
Typereview
Languageen
FieldMedicine
TopicAssisted Reproductive Technology and Twin Pregnancy
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintOffspringMeta-analysisMedicineBiologyPregnancyComputer scienceGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Background: The relationship between assisted reproductive technology (ART) and childhood cancer risk has been widely debated. Previous meta-analyses did not adequately account for the impact of infertility, and this study addresses this gap. Objective: Our primary objective was to assess the relative risk (RR) of childhood cancer in infertile populations using ART versus non-ART offspring, with a secondary focus on comparing frozen embryo transfer (FET) and fresh embryo transfer (fresh-ET). Methods: A literature review was conducted through PubMed, Embase, Cochrane, and Web of Science, with a cutoff date of July 10, 2024. The study was registered with the International Platform of Registered Systematic Review and Meta-Analysis Protocols (INPLASY 202470119). Inclusion criteria were based on the PICOS (Population, Intervention, Comparison, Outcomes, and Study Design) framework: infertile or subfertile couples (population), ART interventions (in vitro fertilization [IVF], intracytoplasmic sperm injection [ICSI], FET, and fresh-ET), non-ART comparison, and childhood cancer risk outcomes. Data abstraction focused on the primary exposures (ART vs non-ART and FET vs fresh-ET) and outcomes (childhood cancer risk). The risk of bias was assessed using the Newcastle-Ottawa Quality Assessment Scale, and the evidence quality was evaluated with the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Pooled estimates and 95% CIs were calculated using random effects models. Results: A total of 18 studies were included, published between 2000 and 2024, consisting of 14 (78%) cohort studies and 4 (22%) case-control studies, all of which were of moderate to high quality. The cohort studies had follow-up periods ranging from 3 to 18 years. Compared with non-ART conception, ART conception was not significantly associated with an increased risk of childhood overall cancer (RR 0.95, 95% CI 0.71-1.27; GRADE quality: low to moderate). Subgroup analyses of IVF (RR 0.86, 95% CI 0.59-1.25), ICSI (RR 0.76, 95% CI 0.26-2.2), FET (RR 0.98, 95% CI 0.54-1.76), and fresh-ET (RR 0.75, 95% CI 0.49-1.15) showed similar findings. No significant differences were found for specific childhood cancers, including leukemia (RR 0.99, 95% CI 0.79-1.24), lymphoma (RR 1.22, 95% CI 0.64-2.34), brain cancer (RR 1.22, 95% CI 0.73-2.05), embryonal tumors (RR 1, 95% CI 0.63-1.58), retinoblastoma (RR 1.3, 95% CI 0.73-2.31), and neuroblastoma (RR 1.02, 95% CI 0.48-2.16). Additionally, no significant difference was observed in a head-to-head comparison of FET versus fresh-ET (RR 0.99, 95% CI 0.86-1.14; GRADE quality: moderate). Conclusions: In conclusion, this study found no significant difference in the risk of childhood cancer between offspring conceived through ART and those conceived through non-ART treatments (such as fertility drugs or intrauterine insemination) in infertile populations. While infertility treatments may elevate baseline risks, our findings suggest that whether individuals with infertility conceive using ART or non-ART methods, their offspring do not face a significantly higher risk of childhood cancer. Further research, especially comparing infertile populations who conceive naturally, is needed to better understand potential long-term health outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.531
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.352
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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