Hair cortisol stability after 5-year storage: Insights from a sample of 17-year-old adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it