Determination of the Glycemic Index of a Weight Loss Meal Plan Program Using Energy Controlled, Pre‐packaged Products
Bibliographic record
Abstract
Background Consumption of low glycemic index (GI) diets has been associated with a range of health benefits including weight loss. Meal plans incorporating pre‐packaged, energy‐controlled, low‐GI products may therefore be a convenient and effective intervention for weight management. However, GI is affected by the degree of food processing. This suggests that a low‐GI diet based on such foods could be difficult to construct. Objective Determine the GI of a meal plan based on a line of prepackaged foods designed to have a low‐GI. Methods Sixty‐six foods, including 16 breakfast, 15 lunch, 13 dinner and 22 snack items, were tested. Groups of ten healthy subjects tested each food once and a control food three times. The GI of test foods was calculated using standard methodology from capillary blood samples obtained at −5, 0, 15, 30, 45, 60, 90, and 120 minutes. GI for the full meal plan was estimated from tested foods and recommended supplemental grocery items. Results 49 foods fell in the low GI range, 13 in the medium and 4 high. Estimated dietary GI of the full meal plan was in the low range (GI = 44). Conclusion This study demonstrated that an energy‐controlled, low‐GI diet can be assembled using a line of pre‐packaged foods, thus providing an option in designing research or clinical interventions requiring a low‐GI diet. Funding Source: Nutrisystem, Inc.
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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".