Technical note: Comparison of 3 methods for analyzing areas under the curve for glucose and nonesterified fatty acids concentrations following epinephrine challenge in dairy cows
Bibliographic record
Abstract
The objective of the study was to compare 3 methods for calculating the area under the curve (AUC) for plasma glucose and nonesterified fatty acids (NEFA) after an intravenous epinephrine (EPI) challenge in dairy cows. Cows were assigned to 1 of 6 dietary niacin treatments in a completely randomized 6 × 6 Latin square with an extra period to measure carryover effects. Periods consisted of a 7-d (d 1 to 7) adaptation period followed by a 7-d (d 8 to 14) measurement period. On d 12, cows received an i.v. infusion of EPI (1.4 μg/kg of BW). Blood was sampled at -45, -30, -20, -10, and -5 min before EPI infusion and 2.5, 5, 10, 15, 20, 30, 45, 60, 90, and 120 min after. The AUC was calculated by incremental area, positive incremental area, and total area using the trapezoidal rule. The 3 methods resulted in different statistical inferences. When comparing the 3 methods for NEFA and glucose response, no significant differences among treatments and no interactions between treatment and AUC method were observed. For glucose and NEFA response, the method was statistically significant. Our results suggest that the positive incremental method and the total area method gave similar results and interpretation but differed from the incremental area method. Furthermore, the 3 methods evaluated can lead to different results and statistical inferences for glucose and NEFA AUC after an EPI challenge.
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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 | 0.047 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".