Effect of Training on the Reliability of Satiety Evaluation and Use of Trained Panellists to Determine the Satiety Effect of Dietary Fibre: A Randomised Controlled Trial
Bibliographic record
Abstract
BACKGROUND: The assessment of satiety effects on foods is commonly performed by untrained volunteers marking their perceived hunger or fullness on line scales, marked with pre-set descriptors. The lack of reproducibility of satiety measurement using this approach however results in the tool being unable to distinguish between foods that have small, but possibly important, differences in their satiety effects. An alternate approach is used in sensory evaluation; panellists can be trained in the correct use of the assessment line-scale and brought to consensus on the meanings of descriptors used for food quality attributes to improve the panel reliability. The effect of training on the reliability of a satiety panel has not previously been reported. METHOD: In a randomised controlled parallel intervention, the effect of training in the correct use of a satiety labelled magnitude scale (LMS) was assessed versus no-training. The test-retest precision and reliability of two hour postprandial satiety evaluation after consumption of a standard breakfast was compared. The trained panel then compared the satiety effect of two breakfast meals containing either a viscous or a non-viscous dietary fibre in a crossover trial. RESULTS: A subgroup of the 23 panellists (n = 5) improved their test re-test precision after training. Panel satiety area under the curve, "after the training" intervention was significantly different to "before training" (p < 0.001). Reliability of the panel determined by intraclass correlation (ICC) of test and retest showed improved strength of the correlation from 0.70 pre-intervention to 0.95 post intervention. The trained "satiety expert panel" determined that a standard breakfast with 5g of viscous fibre gave significantly higher satiety than with 5g non-viscous fibre (area under curve (AUC) of 478.2, 334.4 respectively) (p ≤ 0.002). CONCLUSION: Training reduced between panellist variability. The improved strength of test-retest ICC as a result of the training intervention suggests that training satiety panellists can improve the discriminating power of satiety evaluation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".