Neuropsychiatric outcomes in offspring after fetal exposure to maternal influenza infection during pregnancy: A systematic review
Bibliographic record
Abstract
Increasing evidence suggests that influenza infection in pregnancy may disrupt fetal neurodevelopment. The impact of maternal influenza infection on offspring neuropsychiatric health has not been comprehensively reviewed. We systematically reviewed comparative studies evaluating associations between maternal influenza infection and neuropsychiatric health outcomes in offspring. We searched MEDLINE, EMBASE, CINAHL, and Web of Science for articles published until January 7, 2022. Included were English studies evaluating neuropsychiatric outcomes in offspring aged > 6 months born to women with and without influenza during pregnancy, defined as clinical or laboratory-confirmed influenza illness, or being pregnant during pandemics/epidemics. Of 12,010 records screened, 42 studies were included. Heterogeneity in study design, exposures, and outcome definitions precluded meta-analyses. Four of 14 studies assessing schizophrenia reported adjusted ratio estimates from 0.5 to 8.2; most 95% CIs contained the null value; study quality was high in three of four. Two studies reported an increased risk of schizophrenia with influenza exposure any time during pregnancy (adjusted incidence rate ratio 8.2, 95% CI: 1.4-48.8; adjusted odds ratio 1.3, 95% CI: 1.2-1.5); another reported a reduced risk with first-trimester exposure (adjusted risk ratio 0.5, 95% CI: 0.3-0.9). Seven studies of autism spectrum disorder reported adjusted ratio estimates from 0.9 to 4.0; all 95% CIs included the null value; study quality was high in four. No conclusions could be drawn about the association between exposure to maternal influenza and neuropsychiatric outcomes due to the limited quantity and quality of available research. Large observational studies with long-term follow-up are required to investigate these associations.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".