How to disentangle psychobiological stress reactivity and recovery: A comparison of model-based and non-compartmental analyses of cortisol concentrations
Bibliographic record
Abstract
This article seeks to address the prevailing issue of how to measure specific process components of psychobiological stress responses. Particularly the change of cortisol secretion due to stress exposure has been discussed as an endophenotype of many psychosomatic health outcomes. To assess its process components, a large variety of non-compartmental parameters (i.e., composite measures of substance concentrations at different points in time) like the area under the concentration-time curve (AUC) are utilized. However, a systematic evaluation and validation of these parameters based on a physiologically plausible model of cortisol secretion has not been performed so far. Thus, a population pharmacokinetic (mixed-effects SDE) model was developed and fitted to densely sampled salivary cortisol data of 10 males from Montreal, Canada, and sparsely sampled data of 200 mixed-sex participants from Dresden, Germany, who completed the Trier Social Stress Test (TSST). Besides the two major process components representing (1) stress-related cortisol secretion (reactivity) and (2) cortisol elimination (recovery), the model incorporates two additional, often disregarded components: (3) the secretory delay after stress onset, and (4) deviations from the projected steady-state concentration. The fitted model (R2 = 99%) was thereafter used to investigate the correlation structure of the four individually varying, and readily interpretable model parameters and eleven popular non-compartmental parameters. Based on these analyses, we recommend to use the minimum-maximum cortisol difference and the minimum concentration as proxy measures of reactivity and recovery, respectively. Finally, statistical power analyses of the reactivity-related sex effect illustrate the consequences of using impure non-compartmental measures of the different process components that underlie the cortisol stress response.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".