Fetal anticonvulsant syndromes and polymorphisms in <i>MTHFR</i>, <i>MTR</i>, and <i>MTRR</i>
Bibliographic record
Abstract
The malformations found in fetal anticonvulsant syndromes (FACS) are associated with folic acid deficiency and methylene-tetrahydrofolate reductase (MTHFR) polymorphisms in the general population. To investigate a possible association between FACS and MTHFR genotype, we recruited 200 mothers who had taken anti-epileptic drugs in pregnancy, and delivered at Aberdeen Maternity Hospital over a 26-year period. Clinical findings in the mothers and their 337 children were documented. A clinical algorithm was devised to diagnose FACS objectively. Case-parent triads were genotyped for polymorphisms in MTHFR, serine hydroxymethyl transferase (SHMT1), methionine synthase (MTR), and methionine synthase reductase (MTRR), and analyzed by log-linear regression. No effect of the child's genotype on congenital malformation, neurodevelopmental disorder or FACS was detected using this method. The risk of having a child with congenital malformation or FACS was three to four times higher for mothers who were MTHFR 677TT homozygotes compared with MTHFR 677CC homozygotes. MTR 2756A > G and MTRR 66A > G genotype frequencies in children with FACS and neurodevelopmental disorder were different from those in healthy blood donor controls.
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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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".