Neurocognitive outcomes following fetal exposure to chemotherapy for gestational breast cancer: A Canadian multi-center cohort study
Bibliographic record
Abstract
BACKGROUND: Limited knowledge exists on outcomes of children exposed prenatally to chemotherapy for breast cancer (BC). The purpose of this study was to compare long-term neurocognitive, behavioral, developmental, growth, and health outcomes of children exposed in-utero to chemotherapy for BC. METHODS: This is a multi-center matched cross-sectional cohort study involving seven cancer centers across the region of Southern Ontario (Canada), and the Hospital for Sick Children (Toronto, Ontario). Using standardized psychological and behavioral tests, we compared cognitive and behavioral outcomes in children exposed to chemotherapy during pregnancy for BC to age-matched pairs exposed to known non-teratogens. RESULTS: We recruited 17 parent-child pairs and their matched controls. There were more preterm deliveries in the chemotherapy-exposed group compared to controls (p < 0.05). Full Scale IQ of children in the chemotherapy group was significantly confounded by maternal IQ and prematurity. Exposed children born at term were not different in cognitive outcomes. Children from both groups were similar in their developmental milestones, pediatric anthropometric measurements and health problems. There were no cases of autoimmune cytopenia. CONCLUSIONS: This is the first Canadian prospective comparative study designed to assess pediatric cognition following prenatal exposure to chemotherapy for BC. Chemotherapy was not found to be neurotoxic in this cohort and did not affect pediatric health. The decision to plan a preterm birth for initiating or continuing chemotherapy treatment must be taken into consideration in context of pediatric implications. While these results may assist in such decision making, replication with a larger sample is needed for more conclusive findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".