Measurement of cortisol in saliva: a comparison of measurement error within and between international academic-research laboratories
Bibliographic record
Abstract
OBJECTIVE: Hundreds of scientific publications are produced annually that involve the measurement of cortisol in saliva. Intra- and inter-laboratory variation in salivary cortisol results has the potential to contribute to cross-study inconsistencies in findings, and the perception that salivary cortisol results are unreliable. This study rigorously estimates sources of measurement variability in the assay of salivary cortisol within and between established international academic-based laboratories that specialize in saliva analyses. One hundred young adults (Mean age: 23.10 years; 62 females) donated 2 mL of whole saliva by passive drool. Each sample was split into multiple- 100 µL aliquots and immediately frozen. One aliquot of each of the 100 participants' saliva was transported to academic laboratories (N = 9) in the United States, Canada, UK, and Germany and assayed for cortisol by the same commercially available immunoassay. RESULTS: 1.76% of the variance in salivary cortisol levels was attributable to differences between duplicate assays of the same sample within laboratories, 7.93% of the variance was associated with differences between laboratories, and 90.31% to differences between samples. In established-qualified laboratories, measurement error of salivary cortisol is minimal, and inter-laboratory differences in measurement are unlikely to have a major influence on the determined values.
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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 | 0.080 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".