Toward Standardization of Hair Cortisol Measurement
Bibliographic record
Abstract
BACKGROUND: The importance of hair cortisol as a long-term retrospective measure of systemic cortisol exposure is being increasingly recognized, and over recent years, the field of hair cortisol analysis has seen rapid expansion with laboratories around the globe, integrating hair cortisol analysis into their study designs. These laboratories use different methods of analysis, and presently, no attempt has been made to compare them. To move toward clinical utilization of this novel method, international benchmark reference values must be established. For that end, 4 leading laboratories in hair cortisol testing set up a protocol for comparison of the methods used by them. METHODS: Four immunoassay methods and 2 liquid chromatograph-mass spectrometry (LC-MS/MS) methods were compared by analyzing the same hair samples representing the low, intermediate, and high ranges of hair cortisol concentrations (HCC). RESULTS: HCC determined by the 4 immunoassay methods were highly and positively intercorrelated (r(2) between 0.92 and 0.97; all P < 0.0001) in all comparisons of individual laboratories. Additionally, each laboratory's immunoassay HCC had significant positive correlations (r(2) between 0.88 and 0.97; all P < 0.0001) with each of the 2 LC-MS/MS methods, which produced practically identical results. CONCLUSIONS: This study indicates that laboratories using immunoassays can use a correction factor that will convert results into standard LC-MS/MS equivalents.
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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 | 0.173 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".