<i>Food Products as Vehicles</i>For<i>n</i>-3 Fatty Acid Supplementation
Bibliographic record
Abstract
PURPOSE: An n-3 polyunsaturated fatty acid (PUFA) supplement was incorporated into three food products previously determined to be preferred by cancer patients, and overall acceptability of these foods was evaluated. METHODS: Preliminary testing was performed; an internal panel determined initial acceptability of foods with the supplement added. Taste panel evaluations were held at the Cross Cancer Institute in Edmonton, Alberta. Each participant completed a questionnaire rating aroma, flavour, and overall acceptance on a seven-point hedonic scale (1 = dislike extremely, 7 = like extremely), as well as ability to consume each food daily. RESULTS: Foods were well-liked by patients and non-patients. Mean +/- standard deviation acceptance scores for the three foods were pasta sauce 5.9 +/- 0.94 (n=90), oatmeal 6.1 +/- 0.88 (n=79), and smoothie 5.9 +/- 1.12 (n=126). Overall, 94% of patients and non-patients gave tomato pasta sauce, oatmeal, and the smoothie an acceptance score of at least 5. CONCLUSIONS: The supplement was incorporated successfully into three foods, which were highly accepted by patients with cancer. Further research should focus on incorporating the supplement into flavoured or sweet foods, as these appear most effective. Microencapsulated fish oil in food products may be used as an alternative to fish oil capsules for delivering n-3 PUFA in clinical trials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".