Development of a static in vitro digestion model for determination of glycemic index
Bibliographic record
Abstract
While in vivo methods have been used to determine the glycemic response of food, they are time consuming, costly, and not suitable for large-scale applications. As an alternative, in vitro digestion models offer fast, reproducible results to study food digestion kinetics that are less expensive than conducting human trials. While there are several in vitro glycemic index (GI) methods used to determine the GI of food, most do not employ methods of in vivo testing. Therefore, we used a static in vitro digestive system, the Dedicated Ryerson University In-vitro Digester (DRUID), that simulates both gastric and intestinal conditions to determine the glycemic response of commonly consumed carbohydrate-containing foods. Samples were collected at regular intervals over a 2h residence time after digestion in the intestinal phase of the DRUID. The DRUID-determined GI values were compared to published in vivo GI values. A Bland-Altman plot showed that there was agreement between the GI values determined from the DRUID compared with published in vivo GI values. In conclusion, the in vitro DRUID can reliably and reproducibly determine the GI across a spectrum of carbohydrate-containing foods, and has the potential to predict the digestion kinetics of novel food products in vivo that may promote human health.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".