A systematic review of hair cortisol in healthy adults measured using immunoassays: Methodological considerations and proposed reference values for research
Bibliographic record
Abstract
Hair cortisol concentration (HCC) has shown remarkable promise as a stable, non-invasive measure of systemic cortisol; however, despite methodological advances, the value that would typically be seen in healthy adults has not been established. Therefore, we sought to review the relevant literature to determine a reference value for HCC in healthy (i.e., non-clinical) adults. To this end, we conducted a systematic review of the PubMed, Scopus, and CINAHL databases for studies that measured healthy adult HCC using immunoassay methods, given that these are the most widely accessible analytical tools. To be eligible, studies were required to have been published in English, to have provided relevant descriptive statistics (i.e., means and standard deviations), and to have used a healthy adult human sample. We found 17 studies that met our inclusion criteria; the reports involved 1348 participants with a mean age of about 38 years. Since we identified a large amount of between-study heterogeneity, we completed a random-effect meta-regression analysis and found that test kit vendor was the only significant variable of the model. As a result, when using methodologies from traditional finite mixture distributions to determine reference values for mean and elevated HCC in individual healthy adults, we calculated these estimates for each of the major test kit vendors. Future work will need to determine whether our estimated reference values need to be modified, and these efforts will be greatly assisted by studies that account for potential moderating factors, such as age, sex, and ethnicity.
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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 | 0.036 | 0.146 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".