Reproducibility of Plasma Steroid Hormones, Prolactin, and Insulin-like Growth Factor Levels among Premenopausal Women over a 2- to 3-Year Period
Bibliographic record
Abstract
Few studies have evaluated whether a single blood hormone measurement, as is available in most epidemiologic studies, sufficiently characterizes a premenopausal woman's long-term hormone levels; there is particular concern whether sex steroid hormones, which fluctuate during the menstrual cycle, are reliable. We conducted a prospective study within the Nurses' Health Study II to examine the reproducibility of plasma estrogens, androgens, progesterone, prolactin, sex hormone binding globulin, insulin-like growth factor-I (IGF-I), and IGF binding protein-3 (IGFBP-3). One blood sample per year over 3 years was collected from 113 premenopausal women during both the follicular and luteal phases of the menstrual cycle. We calculated intraclass correlation coefficients (ICC) across the three samples for all women. Among estrogens, ICCs ranged from 0.38 (estradiol) to 0.60 (estrone sulfate) in the follicular phase and from 0.44 (estrone) to 0.69 (estrone sulfate) in the luteal phase. Among androgens, ICCs ranged from 0.58 (androstenedione) to 0.94 [dehydroepiandrostenedione sulfate (DHEAS)] in the follicular phase and from 0.56 (testosterone) to 0.81 (DHEAS) in the luteal phase. When values were averaged across the follicular and luteal phases, the ICC for prolactin was 0.64 whereas ICCs for IGF-I and IGFBP-3 were 0.86 and 0.82, respectively. The ICC for progesterone in the luteal phase was only 0.29. These data suggest that for androgens, estrone sulfate, prolactin, IGF-I, and IGFBP-3, a single measurement can reliably categorize average levels over at least a 3-year period in premenopausal women. For estrone and estradiol, where ICCs were relatively low, it is important to use reproducibility data such as those to correct for measurement error in epidemiologic studies.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".