Dietary intake profile in high-risk pregnant women according to the degree of food processing
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Studies that address dietary intake theme during pregnancy are generally centered on specific nutrients or on dietary patterns. However, the maternal dietary profile according to the degree of food processing is poorly understood. The purpose of the present study was to describe the dietary profile of high-risk pregnant women according to the degree of food processing. MATERIALS AND METHODS: A prospective cohort study was conducted at Prof. Dr. Jose Aristodemo Pinotti Women's Hospital (CAISM), University of Campinas, Brazil, with high-risk pregnant women in the third trimester of gestation. RESULTS: Data from 125 high-risk pregnant women were collected between September 2017 and April 2019. The mean total energy intake (EI) was 1778.3 ± 495.79 kcal/day and the majority of the calories was from unprocessed foods (52.42%), followed by ultra-processed foods (25.46%). The consumption of free sugar and sodium exceeded recommendations, while the consumption of fiber, calcium, folate and iron was below recommendations. The ultra-processed foods intake affects dietary patterns negatively. CONCLUSION: More than 50% of the EI of high-risk pregnant women is from unprocessed or minimally processed foods, but it is insufficient for meeting dairy fiber, iron, folate and calcium recommendations.
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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 | 0.001 |
| 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".