Metabolic Syndrome features and risk of neural tube defects
Bibliographic record
Abstract
BACKGROUND: Maternal obesity and pre-pregnancy diabetes mellitus, features of the metabolic syndrome (MetSyn), are individual risk factors for neural tube defects (NTD). Whether they, in combination with additional features of MetSyn, alter this risk is not known. We evaluated the risk of NTD in association with maternal features of the MetSyn. METHODS: We used a population-based case-control study design in the province of Ontario, Canada. Cases and controls were derived from women who underwent antenatal maternal screening (MSS) at 15 to 20 weeks' gestation. There were 89 maternal cases with, and 434 controls without, an NTD-affected singleton pregnancy. Maternal features of MetSyn were defined by the presence of pre-pregnancy diabetes mellitus, body weight > or = 90th centile among controls, non-white ethnicity and/or serum highly sensitive C-reactive protein (hsCRP) > or = 75th centile of controls. Since hsCRP naturally increases in pregnancy, analyses were performed with, and without, the inclusion of hsCRP in the model. RESULTS: Mean hsCRP concentrations were exceptionally high among study cases and controls (6.1 and 6.4 mg/L, respectively). When hsCRP was excluded from the model, the adjusted odds ratios for NTD were 1.9 (95% confidence interval 1.1-3.4) in the presence 1 feature of MetSyn, and 6.1 (1.1-32.9) in the presence of 2 or more features. When hsCRP was included, the respective risk estimates were attenuated to 1.6 (0.88-2.8) and 3.1 (1.2-8.3). CONCLUSION: We found about 2-fold and 6-fold higher risk for NTD in the presence 1, and 2 or more features, of the metabolic syndrome, respectively. It is not clear whether this risk is altered by the presence of a high serum hsCRP concentration.
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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) | 0.001 | 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".