Strawberries to improve palatability of a cholesterol lowering diet
Bibliographic record
Abstract
Introduction: Effective cholesterol‐lowering diets are often unappealing. To improve the palatability of an effective cholesterol‐lowering combination diet (dietary portfolio), oat bran bread was exchanged for strawberries. Methods: Twenty‐eight hyperlipidemic subjects who had taken the dietary portfolio consisting of soy products, viscous fibers, plant sterols and almonds for mean duration of 1.5 year took additional oat bran bread (65g/d, 112 kcal, ≈ 2g β‐glucan) or strawberries (454g/d, 112 kcal) for one month in random order with a 2 week washout. Results: On a scale of 1 (unpalatable) to 10 (highly palatable) the strawberries had a palatability score of 8.8±0.3 versus oat bran 6.2±0.4 at the end of the respective phase (P<0.001). At week 4 on the strawberry phase the LDL‐C reduction from baseline (1.5 years pre‐study) was 13.3±2.1% (P<0.001) and the total:HDL‐C was 15.7±1.7% (P<0.001). Similar reductions were observed at week 4 on the oat bran of 13.9±2.3% (P<0.001) and 14.6±2.1% (P<0.001), respectively. Conclusion: Strawberries enhanced the palatability of a cholesterol‐lowering diet while maintaining the serum lipid reductions of the dietary portfolio. Research support: California Strawberry Commission.
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 | 0.000 | 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) | 0.005 | 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; 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".