Exposure to ultra-processed foods during pregnancy and ultrasound fetal growth parameters
Bibliographic record
Abstract
Periconceptional maternal ultra-processed food (UPF) consumption impairs embryonic growth. Impacts of exposure to UPF on distinct components of fetal growth in late pregnancy are unknown. We investigated the influence of frequency of UPF consumption during pregnancy on fetal head circumference (HC), abdominal circumference (AC) and femur length (FL). This study included 417 live-born singleton pregnancies prospectively followed-up since the antenatal period in the MINA-Brazil Study, with an available ultrasound scan at >24 gestational weeks. Frequency of food groups consumption in the previous month was categorised as no/monthly, weekly or daily. Ultrasound scans were conducted at 27·8 (sd: 1·7) gestational weeks. HC, AC and FL z-scores were calculated for gestational age using the INTERGROWTH-21st Project standards. Simultaneous-quantile regression models were fitted at the 10th, 50th and 90th percentiles of the distribution of each ultrasound parameter according to UPF consumption, with adjustment for potential confounders. Participants were aged on average 24·7 (sd: 6·5) years, 44·8 % were primiparous, and 26·9 % and 24·9 %, respectively, had weekly and daily UPF consumption. Compared with no/monthly intake, daily UPF consumption impaired HC across its distribution, with significant effect sizes varying from -0·24 to -0·40 z-score. Weekly UPF consumption decreased HC at the 90th percentile by -0·39 z-score (95 % CI: -0·78, -0·01) and FL at the 50th percentile by -0·32 z-score (95 % CI: -0·60, -0·04). No association was noted with AC. Frequency of UPF consumption was negatively associated with skeletal components of fetal growth in late pregnancy. Infant body composition may benefit from healthy food practices since 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".