Hair cortisol stability after 5-year storage: Insights from a sample of 17-year-old adolescents
Bibliographic record
Abstract
Background: Hair has become an increasingly valuable medium to investigate the association between chronic stress, stable differences in systemic cortisol secretion and later health. Assessing cortisol in hair has many advantages, notably its non-invasive and retrospective nature, the need for a single biospecimen and convenient storage until analysis. However, few studies offered empirical evidence documenting the long-term temporal stability of hair cortisol concentration (HCC) prior to analysis, especially in humans. Yet, knowing how long hair samples can be stored without compromising the accuracy of cortisol measurement is of crucial importance when planning data collection and analysis. This study examined the stability of HCC in hair samples assayed twice, five years apart. Methods: We randomly selected from a larger distribution of HCC measured in 17-year-old participants 39 hair samples to be reanalyzed five years later, under the same general conditions. Samples were assayed in duplicate using a luminescence immunoassay and compared with the original HCC using the Lin's concordance correlation coefficient (CCC), Bland-Altman plot analysis and Wilcoxon rank test. Results: Findings indicated a good concordance and temporal stability between the two samples assayed five years apart (CCC [95% confidence interval] = 0.84 [0.72-0.91]), although a small decrease in HCC was noted 5 years later (8.4% reduction, p = 0.001). Conclusion: Our study confirms that hair samples, when stored at room temperature and away from sunlight, can be assayed for at least five years without risking a loss of precision in HCC measurement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".