A Comparison of Prenatal Exposures in Children with and Without a Diagnosis of Autism Spectrum Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The current study was a case-control, focused on the presence of environmental exposures during pregnancy in mothers of children diagnosed with autism spectrum disorder (ASD) and children who were not. Exposures investigated included: acetaminophen/paracetamol use, air pollution, fever, smoking, parental age, maternal diabetes, prenatal vitamin use, workplace exposures, recreational drug use, seafood consumption, obesity, and maternal thyroid issues. Two-hundred and fifteen mothers of children (107 with ASD and 108 without ASD) aged 0-10 years participated in a telephone survey regarding prenatal exposures followed by a chart review. Data were analyzed with a series of univariate tests and a multivariate logistic regression. Univariate analyses showed correlation for the presence of siblings with ASD, presence of family members with ASD, maternal use of medications and maternal smoking during pregnancy; and child's gestational age at the start of prenatal vitamins with a diagnosis of ASD. Multivariate logistic regression analysis demonstrated an association with the use of medications (although specific medications could not be delineated due to small sample size), smoking, and gestational age at the start of prenatal vitamins. These preliminary results suggest that certain prenatal exposures (medication use, smoking, and gestational age at the start of prenatal vitamins) may be associated with a later diagnosis of ASD. Future research should be conducted with larger sample sizes and control for potentially confounding factors. Working towards an understanding of factors that come together to create or prevent a diagnosis of autism will be helpful for families, physicians, and allocating government resources.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it