Measuring indices of lifelong estrogen exposure: self-report reliability
Bibliographic record
Abstract
OBJECTIVE: The utility of clinical markers of lifelong estrogen exposure is established in the understanding of breast cancer, osteoporosis and dementia, among others. However, a good number of studies rely on self-reports to ascertain the involvement of certain estrogen exposure indices. The goal of this study is to assess the reliability of self-reported lifelong estrogen exposure indices by measuring correlation between two repeats. METHODS: A questionnaire assessing lifelong indices of estrogen exposure was developed (revised version included) and completed by 36 healthy postmenopausal women twice within a 4-year interval (age range from 50 to 79 years). Reliability was tested using Pearson's correlation coefficient. RESULTS: Strong significant correlations were observed for most estrogen exposure indices and an effect of age was revealed. Age at menopause and age at initiation of hormone therapy were the two variables leading to weaker correlations across time of measurements; no relation was found between Time 1 and Time 2 when looking at the group of older women (over 65 years of age). CONCLUSIONS: Overall, these results support the use of self-reported measures for most of the lifelong estrogen exposure indices, but they also warn us about the pitfalls of the climacteric period. However, the design of the current study did not allow us to test accuracy; thus, the validity of these self-reported variables needs to be addressed in the future.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".