NUQUEST—NUtrition QUality Evaluation Strengthening Tools: development of tools for the evaluation of risk of bias in nutrition studies
Bibliographic record
Abstract
BACKGROUND: Dietary exposure assessments are a critical issue in evaluating human nutrition studies; however, nutrition-specific criteria are not consistently included in existing bias assessment tools. OBJECTIVES: Our objective was to develop a set of risk of bias (RoB) tools that integrated nutrition-specific criteria into validated generic assessment tools to address RoB issues, including those specific to dietary exposure assessment. METHODS: The Nutrition QUality Evaluation Strengthening Tools (NUQUEST) development and validation process included 8 steps. The first steps identified 1) a development strategy; 2) generic assessment tools with demonstrated validity; and 3) nutrition-specific appraisal issues. This was followed by 4) generation of nutrition-specific items and 5) development of guidance to aid users of NUQUEST. The final steps used established ratings of selected studies and feedback from independent raters to 6) assess reliability and validity; 7) assess formatting and usability; and 8) finalize NUQUEST. RESULTS: NUQUEST is based on the Scottish Intercollegiate Guidelines Network checklists for randomized controlled trials, cohort studies, and case-control studies. Using a purposive sample of 45 studies representing the 3 study designs, interrater reliability was high (Cohen's κ: 0.73; 95% CI: 0.52, 0.93) across all tools and at least moderate for individual tools (range: 0.57-1.00). The use of a worksheet improved usability and consistency of overall interrater agreement across all study designs (40% without worksheet, 80%-100% with worksheet). When compared to published ratings, NUQUEST ratings for evaluated studies demonstrated high concurrent validity (93% perfect or near-perfect agreement). Where there was disagreement, the nutrition-specific component was a contributing factor in discerning exposure methodological issues. CONCLUSIONS: NUQUEST integrates nutrition-specific criteria with generic criteria from assessment tools with demonstrated reliability and validity. NUQUEST represents a consistent and transparent approach for evaluating RoB issues related to dietary exposure assessment commonly encountered in human nutrition studies.
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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 | 0.553 | 0.754 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".