High-level medium-chain triglyceride feeding and energy expenditure in normal-weight women
Bibliographic record
Abstract
The objective of this study was to assess how short-term feeding of high levels of dietary medium-chain triglyceride (MCT) affect energy expenditure and postprandial substrate oxidation rates in normal-weight, premenopausal women. Eight healthy women were fed both a MCT-rich and an isocaloric long-chain triglyceride (LCT)-rich diet for two 1-week periods separated by a minimum of 21 days. The energy intake in each diet was 45% carbohydrates, 40% fat, and 15% protein. The 2 diets had either 60.81% or 1.11% of total fat energy from MCT with the remaining fat energy intake from LCT. On days 1 and 7 of each diet, resting metabolic rate and postprandial energy expenditure (EE) were measured by indirect calorimetry with a ventilated hood. Results indicated on days 1 and 7, there were no significant differences between diets for resting metabolic rate or mean postprandial EE. On both days 1 and 7, fat oxidation for the MCT-rich diet was significantly greater (0.0001 </= p </= 0.04) than that for the LCT-rich diet at different time points across the 5.5 h postprandial period. In conclusion, for premenopausal, normal-weight women consuming a diet with 25% of the energy content from MCT, there were no changes in resting metabolic rate, transient increases in postprandial energy expenditure, and significant increases in postprandial fat oxidation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".