Correlations between salivary- and blood-derived gonadal hormone assessments and implications for inclusion of female participants in research studies
Bibliographic record
Abstract
Even in the 21st century, female participants continue to be underrepresented in human physiology research. This underrepresentation is attributable in part to the perception that the inclusion of females is more time consuming, less convenient, and more expensive relative to males because of the need to account for the menstrual cycle in cardiovascular study designs. Accounting for menstrual cycle-induced fluctuations in gonadal hormones is important, given established roles in governing vascular function and evidence that failure to consider gonadal hormone fluctuations can result in misinterpretations of biomarkers of cardiovascular disease. Thus, for cardiovascular researchers, the inclusion of females in research studies implies a necessity to predict, quantify, and/or track indexes of menstrual cycle-induced changes in hormones. It is here that methodologies are lacking. Gold standard measurement requires venous blood samples, but this technique is invasive and can become both expensive and technically preclusive when serial measurements are required. To this end, saliva-derived measures of gonadal hormones provide a means of simple, noninvasive hormone tracking. To investigate the feasibility of this technique as a means of facilitating research designs that take the menstrual cycle into account, the purpose of this review was to examine literature comparing salivary and blood concentrations of the primary gonadal hormones that fluctuate across the menstrual cycle: estradiol and progesterone. The data indicate that there appear to be valid and promising applications of salivary gonadal hormone monitoring, which may aid in the inclusion of female participants in cardiovascular research studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".