Impact of pregnancy planning and preconceptual dietary training on metabolic control and offspring's outcome in phenylketonuria
Bibliographic record
Abstract
To prevent maternal phenylketonuria (PKU) syndrome low phenylalanine concentrations (target range, 120-360 μmol/L) during pregnancy are recommended for women with PKU. We evaluated the feasibility and effectiveness of current recommendations and identified factors influencing maternal metabolic control and children's outcome. Retrospective study of first successfully completed pregnancies of 85 women with PKU from 12 German centers using historical data and interviews with the women. Children's outcome was evaluated by standardized IQ tests and parental rating of child behavior. Seventy-four percent (63/85) of women started treatment before conception, 64% (54/85) reached the phenylalanine target range before conception. Pregnancy planning resulted in earlier achievement of the phenylalanine target (18 weeks before conception planned vs. 11 weeks of gestation unplanned, p < 0.001) and lower plasma phenylalanine concentrations during pregnancy, particularly in the first trimester (0-7 weeks of gestation: 247 μmol/L planned vs. 467 μmol/L unplanned, p < 0.0001; 8-12 weeks of gestation: 235 μmol/L planned vs. 414 μmol/L unplanned, p < 0.001). Preconceptual dietary training increased the success rate of achieving the phenylalanine target before conception compared to women without training (19 weeks before conception vs. 9 weeks of gestation, p < 0.001). The majority (93%) of children had normal IQ (mean 103, median age 7.3 years); however, IQ decreased with increasing phenylalanine concentration during pregnancy. Good metabolic control during pregnancy is the prerequisite to prevent maternal PKU syndrome in the offspring. This can be achieved by timely provision of detailed information, preconceptual dietary training, and careful planning of pregnancy.
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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 | 0.003 |
| 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) | 0.001 | 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".