Measuring glycaemic responses: duplicate fasting samples or duplicate measures of one fasting sample?
Bibliographic record
Abstract
The precision with which glycaemic responses, expressed as incremental area under the curve (AUC), can be measured may be improved by using the average of several measures of fasting blood glucose (FBG). To see if taking two fasting blood samples would increase the precision of AUC, the glycaemic responses elicited by four test meals (50 g glucose; 50 g glucose plus 10 g fat and 10 g protein; 100 g white bread; 100 g white bread plus 10 g fat and 10 g protein) were determined in thirteen overnight-fasted healthy subjects. Two fasting blood samples were taken 5 min apart (-5 min and 0 min before starting to eat) with glucose measured three times in each sample. AUC was calculated using different estimates of FBG derived from the three measures of glucose in the two fasting blood samples and each set of AUC values subjected to ANOVA. Unexpectedly, the results were more precise when AUC was calculated from mean glucose in the 0 min blood sample (FBG0) than from mean glucose in the two different fasting blood samples. The 95 % CI of the AUC calculated using FBG0 in thirteen subjects was +/-29.8; to obtain the same CI using the mean of the two fasting blood samples would require fourteen subjects. These results suggest that taking two fasting blood samples does not necessarily improve, and may even reduce, the precision of AUC as a measure of glycaemic response. Further studies are needed before requiring that two fasting blood samples be taken for determining glycaemic index.
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 | 0.109 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".