Effect of dried fruit on postprandial glycemia: a randomized acute-feeding trial
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: To investigate the effect of dried fruit in modifying postprandial glycemia, we assessed the ability of 4 dried fruits (dates, apricots, raisins, sultanas) to decrease postprandial glycemia through three mechanisms: a glycemic index (GI) effect, displacement effect, or 'catalytic' fructose effect. SUBJECTS/METHODS: We conducted an acute randomized, multiple-crossover trial in an outpatient setting in 10 healthy adults. Participants received 3 white bread control meals and 12 dried fruit test meals in random order. The test meals included each of 4 dried fruits (dates, apricots, raisins, sultanas) alone (GI effect), 4 of the dried fruits displacing half the available carbohydrate in white bread (displacement effect), or 4 of the dried fruits providing a small 'catalytic' dose (7.5 g) of fructose added to white bread ('catalytic' fructose effect). The protocol followed the ISO method for the determination of GI (ISO 26642:2010). The primary outcome was mean ± SEM GI (glucose scale) for ease of comparison across the three mechanisms. RESULTS: ) were recruited and completed the trial. All dried fruit had a GI below that of white bread (GI = 71); however, only dried apricots (GI = 42 ± 5), raisins (GI = 55 ± 5), and sultanas (51 ± 4) showed a significant GI effect (P < 0.05). When displacing half the available carbohydrate in white bread, all dried fruit lowered the GI; however, only dried apricots (GI = 57 ± 5) showed a significant displacement effect (P = 0.025). None of the dried fruits showed a beneficial 'catalytic' fructose effect. CONCLUSIONS: In conclusion, dried fruits have a lower GI and reduce the glycemic response of white bread through displacement of half of the available carbohydrate. Longer-term randomized trials are needed to confirm whether dried fruit can contribute to sustainable improvements in glycemic control. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT02960373.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".