Effect of blood sampling schedule and method of calculating the area under the curve on validity and precision of glycaemic index values
Bibliographic record
Abstract
To evaluate the suitability for glycaemic index (GI) calculations of using blood sampling schedules and methods of calculating area under the curve (AUC) different from those recommended, the GI values of five foods were determined by recommended methods (capillary blood glucose measured seven times over 2.0 h) in forty-seven normal subjects and different calculations performed on the same data set. The AUC was calculated in four ways: incremental AUC (iAUC; recommended method), iAUC above the minimum blood glucose value (AUCmin), net AUC (netAUC) and iAUC including area only before the glycaemic response curve cuts the baseline (AUCcut). In addition, iAUC was calculated using four different sets of less than seven blood samples. GI values were derived using each AUC calculation. The mean GI values of the foods varied significantly according to the method of calculating GI. The standard deviation of GI values calculating using iAUC (20.4), was lower than six of the seven other methods, and significantly less (P<0.05) than that using netAUC (24.0). To be a valid index of food glycaemic response independent of subject characteristics, GI values in subjects should not be related to their AUC after oral glucose. However, calculating GI using AUCmin or less than seven blood samples resulted in significant (P<0.05) relationships between GI and mean AUC. It is concluded that, in subjects without diabetes, the recommended blood sampling schedule and method of AUC calculation yields more valid and/or more precise GI values than the seven other methods tested here. The only method whose results agreed reasonably well with the recommended method (ie. within +/-5 %) was AUCcut.
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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 | 0.071 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".