A systematic review of reviews identifying UK validated dietary assessment tools for inclusion on an interactive guided website for researchers: www.nutritools.org
Bibliographic record
Abstract
Background: Health researchers may struggle to choose suitable validated dietary assessment tools (DATs) for their target population. The aim of this review was to identify and collate information on validated UK DATs and validation studies for inclusion on a website to support researchers to choose appropriate DATs. Design: A systematic review of reviews of DATs was undertaken. DATs validated in UK populations were extracted from the studies identified. A searchable website was designed to display these data. Additionally, mean differences and limits of agreement between test and comparison methods were summarized by a method, weighting by sample size. Results: Over 900 validation results covering 5 life stages, 18 nutrients, 6 dietary assessment methods, and 9 validation method types were extracted from 63 validated DATs which were identified from 68 reviews. These were incorporated into www.nutritools.org. Limits of agreement were determined for about half of validations. Thirty four DATs were FFQs. Only 17 DATs were validated against biomarkers, and only 19 DATs were validated in infant/children/adolescents. Conclusions: The interactive www.nutritools.org website holds extensive validation data identified from this review and can be used to guide researchers to critically compare and choose a suitable DAT for their research question, leading to improvement of nutritional epidemiology research.
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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 | 0.022 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".