Clinical experimental stress studies: methods and assessment
Bibliographic record
Abstract
Stress is a state of threatened homeostasis during which a variety of adaptive processes are activated to produce physiological and behavioral changes. Stress induction methods are pivotal for understanding these physiological or pathophysiological changes in the body in response to stress. Furthermore, these methods are also important for the development of novel pharmacological agents for stress management. The well-described methods to induce stress in humans include the cold pressor test, Trier Social Stress Test, Montreal Imaging Stress Task, Maastricht Acute Stress Test, CO2 challenge test, Stroop test, Paced Auditory Serial Addition Task, noise stress, and Mannheim Multicomponent Stress Test. Stress assessment in humans is done by measuring biochemical markers such as cortisol, cortisol awakening response, dexamethasone suppression test, salivary α-amylase, plasma/urinary norepinephrine, norepinephrine spillover rate, and interleukins. Physiological and behavioral changes such as galvanic skin response, heart rate variability, pupil size, and muscle and/or skin sympathetic nerve activity (microneurography) and cardiovascular parameters such as heart rate, blood pressure, and self-reported anxiety are also monitored to assess stress response. This present review describes these commonly employed methods to induce stress in humans along with stress assessment methods.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".