Assessing risk of neurodevelopmental disorders after birth with oxytocin: a systematic review and meta-analysis
Bibliographic record
Abstract
Experts have raised concerns that oxytocin for labor induction and augmentation may have detrimental effects on the neurodevelopment of children. To investigate whether there is the reason for concern, we reviewed and evaluated the available evidence by searching databases with no language or date restrictions up to 9 September 2018. We included English-language studies reporting results on the association between perinatal oxytocin exposure and any cognitive impairment, psychiatric symptoms or disorders in childhood. We assessed the quality of studies using the Newcastle-Ottawa Quality Assessment Scales. Independent risk estimates were pooled using random-effects meta-analyses when at least two independent datasets provided data on the same symptom or disorder. Otherwise, we provided narrative summaries. Two studies examined cognitive impairment, one examined problem behavior, three examined attention-deficit/hyperactivity disorder (ADHD) and seven focused on autism spectrum disorders (ASD). We provided narrative summaries of the studies on cognitive impairment. For ADHD, the pooled risk estimate was 1.17; 95% confidence interval (CI) 0.77-1.78, based on a pooled sample size of 5 47 278 offspring. For ASD, the pooled risk estimate was 1.10; 95% CI 1.04-1.17, based on 8 87 470 offspring. Conclusions that perinatal oxytocin increases the risks of neurodevelopmental problems are premature. Observational studies of low to high quality comprise the evidence-base, and confounding, especially by the genetic or environmental vulnerability, remains an issue. Current evidence is insufficient to justify modifying obstetric guidelines for the use of oxytocin, which state that it should only be used when clinically indicated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".