Use of the Glycemic Index: Effects on Feeding Patterns and Exercise Performance
Bibliographic record
Abstract
The focus of this paper is on the glycemic index (GI) that provides effectual information on planning nutritional strategies for carbohydrate (CHO) supplementation in exercise. Related research has suggested that the GI can be used as a reference guide for the selection of an ideal CHO supplement in sports nutrition. Recently, the manipulation of GI of CHO supplementation in optimizing athletic performance has provided an exciting new research area in sports nutrition. There is a growing evidence to support the use of the GI in planning the nutritional strategies for CHO supplementation in sports. The optimum CHO availability for exercise has been demonstrated by manipulating the GI of CHO. Research has shown that a low GI CHO-rich meal is a suitable CHO source before prolonged exercise in order to promote the availability of the sustained CHO. In contrast, a high GI CHO-rich meal appears to be beneficial for glycogen storage after the exercise by promoting greater glucose and insulin responses. The prescribed feeding patterns of CHO intake during recovery and prior to exercise on glycogen re-synthesis and exercise metabolism have been studied in the literature. However, the studies on the subject are still limited, leaving some open questions waiting for further empirical evidences. The most significant question is whether CHO supplementation before and after exercise is beneficial when consumed as large feedings or as a series of snacks. Further research is needed on the effect of feeding patterns on exercise performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".