Autism Spectrum Disorders and Maternal Serum alpha-Fetoprotein Levels during Pregnancy
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have been trying to disentangle the complex pathophysiology of autism spectrum disorders (ASD). In our study, we explored the potential role of maternal serum (MS) alpha-fetoprotein (AFP) in the prediction and the pathophysiology of ASD. METHODS: A total of 112 patients with ASD and 243 control subjects were included in a case-control study, using a historic birth cohort maintained at Statens Serum Institute. Measurements of MS-AFP were obtained from a multicentre screening program, whereas clinical data were obtained from nationwide registers. Association between MS-AFP and ASD status was analyzed using logistic regression models and nonparametric tests. RESULTS: Crude, but not adjusted, estimates showed that MS-AFP levels were slightly, but significantly, higher in mothers of children with ASD, compared with their control subject counterparts. People with ASD had an odds ratio of 2.33, with 95% confidence intervals of 1.00 to 5.39, to have MS-AFP above 2.5 multiple of median. Excluding subjects with congenital malformation comorbidities did not alter the direction of our estimates (OR 2.60; 95% CI 1.04 to 6.51, P = 0.04). CONCLUSION: Biologic plausibility of its role in the pathophysiology of ASD makes AFP a good candidate for further larger-scale studies to confirm such an association and to determine whether this pattern is unique to ASD or related to other psychiatric disorders as well.
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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 | 0.005 |
| 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".