Acceptance of oat‐based beverages tailored for patients with cancer
Bibliographic record
Abstract
Oat-based beverages are a nutritious product with the potential to support increased nutrient intake of patients with cancer. The aim of this research was to evaluate the sensory acceptance of oat-based beverages and perceptions of oats among patients with cancer as future vehicles for nutrient delivery. In study 1, three flavors of oat-beverages were well accepted without significant difference in liking among flavors or serving temperature, or between patients with cancer and healthy participants. Patients with cancer more frequently rated the beverages as too sweet compared to healthy participants; flavor intensity was just about right for all participants. In the second study, one of two formulations fortified with protein and fish oil was not different in liking compared to the unfortified chocolate product. Patients associated oat food products with specific oat-based food products and oat health benefits in a free-word association task in the third study. Together, sensory acceptance and the perceived health benefits of oats indicate the potential for oats to be incorporated in fortified and unfortified products tailored for patients with cancer. PRACTICAL APPLICATION: The three studies presented here to assess the sensory acceptance of oat-based beverages and perceptions of oats among patients with cancer demonstrate that oats can be incorporated in fortified and unfortified products tailored for patients with cancer. Inadequate nutrition is highly prevalent among oncology patients and there is a lack of available products targeted to improve their nutritional intake. These findings can support product developers and sensory scientists in the development and evaluation of food products acceptable to this population.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".