A cross-sectional comparison of the functionality of the short-form FFQ to a 3-day food intake record completed early in the second trimester of pregnancy
Bibliographic record
Abstract
Abstract Using a behavioural intervention to target nutrition during pregnancy may be key in meeting recommendations for healthy eating. The aim was to assess the use of a short-term dietary intake measurement tool (3-day food intake record) to infer long-term habitual dietary intake during pregnancy (using a short-form FFQ). A convenience sample ( n 90) between 12- and 18-weeks’ gestation was recruited from a larger randomised controlled trial for cross-sectional analysis. Participants completed a forty-four-item FFQ and 3-day food intake record. Using the participant food intake record, the investigator blindly completed a second frequency questionnaire. The frequency questionnaires were scored using dietary quality scores (DQS) and compared. Aggregate data were evaluated using a Wilcoxon signed rank test, and individual-level data were evaluated using a Bland–Altman plot. No significant difference was observed in the scores ( Z = –1·88, P = 0·06), with small effect size ( r = 0·19). The Bland–Altman plot showed that comparing the DQS derived from the two different dietary assessments underestimated scores by a mean difference of 0·4 points (95 % limits of agreement: −3·50 to 4·26). The data points were evenly spread suggesting no systematic variation for over- or underestimation of scores. Minimal difference was observed between the functionality of the two assessment instruments. However, the food intake record can be completed by pregnant individuals to estimate short-term nutrient intake and then scored by the investigator to estimate long-term dietary quality. Combining these two instruments may best capture the most accurate representation of dietary habits over time.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".