Acute Stress Assessment From Excess Cortisol Secretion: Fundamentals and Perspectives
Bibliographic record
Abstract
Our paper aims to redefine the concept of stress in the context of maintaining allostasis; the term has been reserved for situations that concomitantly involve established physiological and psychological stress components. In particular, we analyze how novelty, unpredictability, threat to the ego, and low sense of control challenge allostasis. The concept of stress is then related to a state of difficulty in maintaining allostasis, rather than referring to the overall body response to the situation. This state of difficulty may be observed either in planning the strategy to deal with the situation, evaluating consequent target trajectories for the actuators, the catabolic mediators and the activators, or regulation of the biological systems through these trajectories. Catabolic mediator excesses are proposed as scaling the level of difficulty in maintaining allostasis. The excess proportion of cortisol load (EPCL) is consequently proposed to scale the stress level. A first proof-of-concept of this indicator is realized using the Physiostress dataset, by asserting that it is, as predicted from its theoretical basis, more in phase with the stress level expected from the nature of the task and participant-reported stress compared to common indicators based on the cortisol response magnitude itself.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".