Comparison of glucose tolerance tests to detect the insulin sensitizing effects of a bout of continuous exercise
Bibliographic record
Abstract
The aim of the present study was to determine which of the available glucose tolerance tests (oral (OGTT) vs. intravenous (IVGTT)) could more readily detect the insulin sensitizing effects of a bout of continuous exercise. Ten healthy moderately fit young men (V̇O2peak of 45.4 ± 1.8 mL·kg(-1)·min(-1); age 27.5 ± 2.7 yr) underwent 4 OGTT and 4 IVGTT on different days following a standardized dinner and overnight fast. One test was performed immediately after 55 min of cycle-ergometer exercise at 60% V̇O2peak. Insulin sensitivity index was determined during a 50 min IVGTT according to Tura (CISI) and from a 120 min OGTT using the Matsuda composite index (MISI). After exercise, MISI improved 29 ± 10% without reaching statistical significance (p = 0.182) due to its low reproducibility (coefficient of variation 16 ± 3%; intra-class reliability 0.846). However, CISI significantly improved (50 ± 4%; p < 0.001) after exercise showing better reproducibility (coefficient of variation 13 ± 4%; intra-class reliability 0.955). Power calculation revealed that 6 participants were required for detecting the effects of exercise on insulin sensitivity when using IVGTT, whereas 54 were needed when using OGTT. The superior response of CISI compared with MISI suggests the preferential use of IVGTT to assess the effects of exercise on insulin sensitivity when using a glucose tolerance test.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".