Exogenous and endogenous estrogen affects the biological components of variance of plasma retinol but not of the RBP:TTR index
Bibliographic record
Abstract
We examined the effects of oral contraceptive (OC) use and endogenous female sexual hormones on the intra‐(SDi) and inter‐individual (SDg) variations and calculated the index of individuality (SDi/SDg) of retinol and RBP:TTR in 20 clinically healthy, non‐smoking females aged 19–30 y. Retinol was measured by HPLC, RBP and TTR by RID, and estradiol and progesterone by RIA in 7 blood samples collected on 4 consecutive d and weekly for a month. The SDi and SDg were calculated using variance component analysis. Females were stratified into 3 groups (OC (n=10), follicular (n=4), and luteal (n=6)) based on hormonal concentrations on d 1, and confirmed by their monthly hormonal patterns. Retinol concentrations were higher in OC than in the other 2 groups (P<0.05), while RBP:TTR was not different. The SDi of retinol in OC group differed significantly from luteal group, whereas that of RBP:TTR index did not. The SDg of retinol was 0.62 (OC), 0.48 (follicular), and 0.17 (luteal), while that of RBP:TTR was 0.06 in each of the 3 groups. Most (90%) of the females who had their retinol concentrations above the population median (2.39 μmol/L) were taking OC and thus had high SDg. For RBP:TTR, though 70% of OC had their values above the median of 0.50, it did not affect SDg because RBP and TTR had similar variations. The index of individuality of retinol was <1 in all 3 groups, while that of the RBP:TTR index was >1 in each group. These results demonstrated that stratifying by hormonal status does not reduce the inter‐individual variation of retinol. (Supported by MI, Canada)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".