The application of the glycemic index and glycemic load in weight loss: A review of the clinical evidence
Bibliographic record
Abstract
Obesity is rapidly becoming a global epidemic. As it is a significant risk factor for several chronic diseases, including type 2 diabetes and cardiovascular disease, it is imperative to study dietary and lifestyle approaches that help reduce its prevalence. Recently, due to its possible link to appetite control and metabolism, several clinical studies have assessed the effect of low glycemic index (GI) and glycemic load (GL) diets on weight loss. To determine the application of GI/GL in the prevention and treatment of obesity, we searched several databases and identified 23 clinical trials that examined low GI/GL diets and weight loss as the primary outcome measure. In general, these studies showed much inconsistency in their findings. While a few studies found significantly greater weight loss on the low GI/GL diets, most of the other studies showed a non-significant trend that favored low GI/GL diets; suggesting that factors other than GI/GL may play a role. It would be helpful if a pooled analysis were undertaken to clarify the current findings and outline the limitations of these studies. There is also a need for more long-term randomized, controlled trials that not only focus on weight loss but also on weight maintenance and body composition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".