Comparing immunoassay and mass spectrometry techniques for salivary sex hormone analysis
Bibliographic record
Abstract
From a confluence of events, our team acquired salivary sex hormone data from two different assays; namely, enzyme-linked immunosorbent immunoassay (ELISA; Salimetrics) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). As previous research has often discussed inter-assay differences but lacked direct comparative data for these specific hormones in saliva, this paper compared both techniques on their ability to accurately quantify concentrations of estradiol, progesterone, and testosterone in healthy young adults (72 combined oral contraceptive [COC] users, 99 naturally cycling [NC] women in the early follicular and pre-ovulatory phases, and 47 men). Using multivariate and computational approaches, our results converged and showed poor performance of ELISA for measuring salivary sex hormones, with estradiol and progesterone being much less valid than testosterone. Despite its challenges with quantification, LC-MS/MS was found to be superior. Our study underscores the importance of methodological rigor in sex steroid hormone assay techniques, highlighting LC-MS/MS as a more reliable option compared to ELISA for salivary sex hormone quantification in healthy adults. These findings contribute to the ongoing dialogue in the field concerning the validity and reproducibility of scientific discoveries. Indeed, accurate measurement is crucial for generating reliable findings regarding the intricate relationships between hormones, brain, behavior, and mental health. • We assessed ELISA and LC-MS/MS performance for measuring salivary sex hormones. • The between-methods relationship was strong for salivary testosterone only. • LC-MS/MS showed expected differences in estradiol and testosterone in women. • Machine-learning classification models revealed better results with LC-MS/MS. • LC-MS/MS promises to improve validity of sex steroid profiling of healthy adults.
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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 | 0.027 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".