Human hair cortisol analysis: Comparison of the internationally-reported ELISA methods
Bibliographic record
Abstract
PURPOSE: Recently, hair cortisol has become a topic of global interest as a biomarker of chronic stress. Different research groups have been using different methods for extraction and analysis, making it difficult to compare results across studies. A critical examination of the reported analytical methods is important to facilitate standardization and allow for a uniform interpretation. METHODS: This study qualitatively compared four published procedures from laboratories in Germany, the Netherlands, USA and Canada. Multiple aspects of their procedures were compared. RESULTS: A major difference among the laboratories was the ELISA kit used: the Canadian laboratory used the kit from ALPCO Diagnostics (Salem, MA, USA), the American laboratory used the kit from DRG International (Springfield, NJ, USA), the German laboratory used the kit from DRG Instruments GmbH (Marburg, Germany), or IBL (Hamburg, Germany), and the Dutch used the kit from Salimetrics (Suffolk, UK). In addition, there are noted differences in hair mass used as well as washing and extraction procedures. The range of hair cortisol levels determined in healthy volunteers by the four groups was within 2.3-fold: Koren, 46.1 pg/mg; Van Rossum, 29.72 pg/mg; Kirschbaum, 20 pg/mg and Laudenslager ~ 27 pg/mg. CONCLUSIONS: The relative similarities in hair cortisol values in volunteers among the four laboratories should facilitate a quality assurance exchange program, as a necessary step toward clinical use of this novel test.
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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 | 0.028 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".