Validity of triple- and dual-tracer techniques to estimate glucose appearance
Bibliographic record
Abstract
The triple-tracer (TT) dilution technique has been proposed to be the gold standard method to measure postprandial glucose appearance. However, validation against an independent standard has been missing. We addressed this issue and also validated the simpler dual-tracer (DT) technique. Sixteen young subjects with type 1 diabetes (age 19.5 ± 3.8 yr, BMI 23.4 ± 1.5 kg/m(2), HbA(1c) 8.7 ± 1.7%, diabetes duration 9.0 ± 6.9 yr, total daily insulin 0.9 ± 0.2 U·kg(-1)·day(-1), mean ± SD) received a variable intravenous 20% dextrose infusion enriched with [U-(13)C]glucose over 8 h to achieve postprandial-resembling glucose excursions while intravenous insulin was administered to achieve postprandial-resembling levels of plasma insulin. Primed [6,6-(2)H(2)]glucose was infused in a manner that mimicked the expected endogenous glucose production and [U-(13)C; 1,2,3,4,5,6,6-(2)H(7)]glucose was infused in a manner that mimicked the expected glucose appearance from a standard meal. Plasma glucose enrichment was measured by gas chromatography-mass spectrometry. The intravenous dextrose infusion served as an independent standard and was reconstructed using the TT and DT techniques with the two-compartment Radziuk/Mari model and an advanced stochastic computational method. The difference between the infused and reconstructed dextrose profile was similar for the two methods (root mean square error 6.6 ± 1.9 vs. 8.0 ± 3.5 μmol·kg(-1)·min(-1), TT vs. DT, P = NS, paired t-test). The TT technique was more accurate in recovering the overall dextrose infusion (100 ± 9 and 92 ± 12%; P = 0.02). The root mean square error associated with the mean dextrose infusion profile was 2.5 and 3.3 μmol·kg(-1)·min(-1) for the TT and DT techniques, respectively. We conclude that the TT and DT techniques combined with the advanced computational method can measure accurately exogenous glucose appearance. The TT technique tends to outperform slightly the DT technique, but the latter benefits from reduced experimental and computational complexity.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".