The Use of Assisted Reproductive Technology by European Childhood Cancer Survivors
Bibliographic record
Abstract
CCS often wish to have biological children yet harbour concerns about fertility impairment, pregnancy risks and the general health risks of prospective offspring. To clarify these concerns, health outcomes in survivor offspring born following ART (n = 74, 4.5%) or after spontaneous conception (n = 1585) were assessed in our European offspring study by descriptive and bivariate analysis. Outcomes were compared to a sibling offspring cohort (n = 387) in a 4:1 matched-pair analysis (n = 1681). (i) Survivors were more likely to employ ART than their siblings (4.5% vs. 3.7%, p = 0.501). Successful pregnancies were achieved after a median of one cycle with, most commonly, intracytoplasmic sperm injection (ICSI) using non-cryopreserved oocytes/sperm. (ii) Multiple-sibling births (p < 0.001, 29.7% vs. 2.5%), low birth weight (p < 0.001; OR = 3.035, 95%-CI = 1.615−5.706), and preterm birth (p < 0.001; OR = 2.499, 95%-CI = 1.401−4.459) occurred significantly more often in survivor offspring following ART utilisation than in spontaneously conceived children. ART did not increase the prevalence of childhood cancer, congenital malformations or heart defects. (iii) These outcomes had similar prevalences in the sibling population. In our explorative study, we could not detect an influence on health outcomes when known confounders, such as multiple births, were taken into account.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".