Assessment of the cortisol awakening response: Expert consensus guidelines
Bibliographic record
Abstract
The cortisol awakening response (CAR), the marked increase in cortisol secretion over the first 30-45 min after morning awakening, has been related to a wide range of psychosocial, physical and mental health parameters, making it a key variable for psychoneuroendocrinological research. The CAR is typically assessed from self-collection of saliva samples within the domestic setting. While this confers ecological validity, it lacks direct researcher oversight which can be problematic as the validity of CAR measurement critically relies on participants closely following a timed sampling schedule, beginning with the moment of awakening. Researchers assessing the CAR thus need to take important steps to maximize and monitor saliva sampling accuracy as well as consider a range of other relevant methodological factors. To promote best practice of future research in this field, the International Society of Psychoneuroendocrinology initiated an expert panel charged with (i) summarizing relevant evidence and collective experience on methodological factors affecting CAR assessment and (ii) formulating clear consensus guidelines for future research. The present report summarizes the results of this undertaking. Consensus guidelines are presented on central aspects of CAR assessment, including objective control of sampling accuracy/adherence, participant instructions, covariate accounting, sampling protocols, quantification strategies as well as reporting and interpreting of CAR data. Meeting these methodological standards in future research will create more powerful research designs, thus yielding more reliable and reproducible results and helping to further advance understanding in this evolving field of research.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| 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".