A Comprehensive Review of Estradiol, Progesterone, Luteinizing Hormone, and Follicle-Stimulating Hormone in the Context of Laboratory Medicine to Support Women's Health
Bibliographic record
Abstract
BACKGROUND: There have been conflicting messages about the influence of female sex hormones on women's health, with historical messaging indicating that use of estrogen and/or progesterone in peri- or postmenopause poses a significant clinical risk to cisgender women. More recent guidance indicates that the benefit of hormone therapy (HT) outweighs the risks for symptomatic women. Exogenous estrogen use is also indicated for contraception and gender-affirming care. Despite the potential for broad applications, robust reference intervals for estradiol, progesterone, luteinizing hormone (LH) and follicle-stimulating hormone (FSH) are lacking, and guidelines indicate that measurement of 17-β-estradiol (E2), progesterone, LH, or FSH does not facilitate care in women who may be experiencing menopausal symptoms or women taking exogenous HT. CONTENT: Here we review the physiological roles of estrogen, progesterone, LH, and FSH. We examine the modes of administration for estrogen and progesterone, clarify the nomenclature related to exogenous hormone use, and comprehensively review the literature for studies evaluating normal concentrations of these female gonadal axis hormones during the menstrual cycle. The content primarily focuses on cisgender women, but some aspects of these hormones in transgender women will also be discussed. SUMMARY: Currently, E2, LH, FSH, and progesterone reference intervals for women remain incomplete. Although there are a variety of clinical indications that benefit women using HT, symptoms and shared decision-making should guide care. Collaborative efforts between clinicians and laboratory professionals to better define therapeutic or reference intervals for these hormones can advance women's health globally.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".