Optimal time point to measure postprandial hypertriglyceridemia after a high-fat meal
Bibliographic record
Abstract
Objective To study the change of postprandial triglyceride (TG) concentrations after a high-fat meal in patients with coronary heart diseases (CHD), patients with essential hypertension (EHP) and healthy controls, and to explore the optimal time point to measure postprandial TG metabolism. Methods Fifty-four CHD patients, thirty-six EHP patients and twenty-five healthy controls were recruited. The concentrations of serum TG in fasting state and at 2, 4, 5, 7 h after a single high-fat meal (800 kcal, including 50 g fat) were measured. Results The postprandial serum TG concentrations increased significantly at 2, 4, 5 h point in all subjects (all P0.05). The CHD patients and EHP patients showed significantly higher and prolonged postprandial TG concentrations than the controls did (all P0.05). The area under TG curve (AUC_ TG ) over 7 h was increased in the order of the controls, the EHP patients and the CHD patients (all P0.05). The required time to reach peak value of TG was obviously delayed in the CHD patients and EHP patients compared with the controls (P0.05). AUC_ TG was most obviously correlated with the increment at 4 h serum TG concentrations in the CHD patients and EHP patients (both r=0.94, P0.001). Conclusion In high-fat meal test, the increment of serum TG concentration at 4 h can be considered as a surrogate of AUC_ TG in CHD and EHP patients.
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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) | 0.001 | 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".