A randomized, double-blind, placebo-controlled, cross-over trial to evaluate the effect of EstroSense <sup>®</sup> on 2-hydroxyestrone:16α-hydroxyestrone ratio in premenopausal women
Bibliographic record
Abstract
Abstract Objectives Some estrogen metabolites are associated with increased breast cancer risk, while others are protective. Research efforts have focused on modifiable factors, including bioactive compounds found in food or supplements, promoting estrogen profiles with anti-cancer properties. EstroSense ® is a nutraceutical product with bioactive compounds, including Indole-3-carbinol and green-tea catechins, which may favourably affect estrogen profiles. This study was conducted to determine if EstroSense use, compared to placebo, promotes a higher urinary 2-hydroxyestrone:16α-hydroxyestrone ratio (2-OHE 1 :16α-OHE 1 ), a biomarker associated with a lowered risk of breast cancer. Methods A total of 148 premenopausal women were recruited from British Columbia, Canada to participate in a randomized, double-blind, cross-over, multicentre, placebo-controlled study in which women were randomized to a treatment sequence that consisted of either EstroSense ® , followed by placebo or vice-versa. The women were instructed to consume three capsules per day of EstroSense ® or the placebo for three menstrual cycles (∼12 weeks). The primary outcome was the measurement of 2-OHE1:16α-OHE1 in casual samples at baseline and after each treatment phase. Results After 12 weeks of intervention, the mean (95% CI) urinary 2-OHE 1 :16α-OHE 1 was 4.55 (2.69, 6.42) (p<0.001) higher following EstroSense than placebo adjusted for baseline values. Conclusions EstroSense use led to markedly higher urinary 2-OHE1:16α-OHE1 than the placebo, a biomarker associated with a lower risk of breast cancer. Registration http://clinicaltrials.gov (NCT02385916).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".