Preprint: Unstable correspondence between salivary testosterone measured with enzyme immunoassays and tandem mass spectrometry
Bibliographic record
Abstract
Although some studies reveal that saliva handling and storage practices may influence salivary testosterone concentrations measured with immunoassays, the effect of these method factors on the validity of testosterone immunoassays remains unknown. The validity of immunoassays can be assessed by comparing hormone concentrations measured with immunoassays to a standard reference method: liquid chromatography tandem mass spectrometry (MS). We previously reported the correspondence between salivary testosterone measured with enzyme immunoassays (EIAs) and with MS when there was less variance in (or more control over) method factors related to saliva handling and storage across measurement methods (Welker et al., 2016). In the present study, we expanded the original dataset and compared the correspondence between Salimetrics EIAs and MS when there was greater variance in (or less control over) method factors across EIAs and MS (high method variance), to when there was less variance in these factors (low method variance). If variance in these method factors impacts the validity of testosterone measurement, then the EIA-MS correspondence should be stronger when method variance is low compared to when it is high. Our results contradicted this hypothesis: Salimetrics EIA-MS correspondence was stronger when variance in method factors was high compared to when it was low. The composite average correlation across both method variance comparisons provides an updated estimate of Salimetrics EIA-MS correspondence, but the instability in this correspondence may pose challenges to the reproducibility of psychoneuroendocrinology research. We discuss possible explanations for the surprising pattern of results and provide recommendations for future research.
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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 | 0.023 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".