Optimizing clinical trial design for assessing the efficacy of functional foods
Bibliographic record
Abstract
Randomized clinical trial data are capable of providing strong experimental evidence to establish causal relationships between functional food components and health and disease/disease risk. However, clinical studies must be well designed in order to optimize the quality of the data they provide. The purpose of this review is to identify design elements that maximize the quality of clinical trials examining the efficacy of functional foods. Both observational studies and experimental trials can provide useful data for identifying diet-disease relationships. Two experimental designs are conventionally used: parallel and crossover. Each of these designs possesses advantages and disadvantages. For certain functional ingredients, selection of an appropriate control arm is straightforward, while for others it is challenging. Studies should be short enough to optimize subject compliance, be cost effective, and avoid high subject dropout rates, while being lengthy enough to ensure biological efficacy. The dose, frequency, and diurnal timing of intake of the active food ingredient all need to be chosen carefully. Randomized clinical trials testing the efficacy of functional foods may use both validated and emerging surrogate endpoints and should employ suitable statistical tests for data analysis. Paying attention to all these factors is crucial to the design of quality clinical trials that reliably evaluate food-health relationship validity. Accordingly, clinical studies that incorporate the optimal design elements discussed will yield robust results appropriate for the substantiation of health claims on functional foods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.344 | 0.496 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".