Premenopausal Levels of Circulating Insulin-Like Growth Factor I and the Risk of Post-Menopausal Breast Cancer: A Population-Based, Nested Case-Control Study
Bibliographic record
Abstract
High levels of circulating IGF-l may be a risk factor for breast cancer. Only one population-based epidemiologic study of IGF-l and breast cancer measured circulating IGF-l in serum drawn prior to diagnosis. For post-menopausal breast cancer cases, no association was found. However, these analyses did not include IGF-l measures from the pre-menopausal period when endogenous IGF-l exposure is naturally higher and, potentially, more predictive of breast cancer risk. This study is a nested case-control analysis of archived serum samples and existing questionnaire data from the CLUE studies. 5,290 women participated in the prospective CLUE studies -- 129 developed a first, incident invasive breast cancer between 1990 and 1998. Cases diagnosed premenopausally will be excluded. One control will be matched to each case on age, menopausal status, age at menopause, follow-up time, date of each blood draw. Samples will be sent to the lab of Dr. Michael Pollack at McGill University. Plasma IGF-l and IGFBP-3 concentrations will be determined by enzyme-linked immunoabsorbent assay (ELISA) . Statistical analyses will: 1) estimate the association between premenopausal IGF-l levels (with and without adjustment for IGFBP-3) on postmenopausal breast cancer risk; and 2) determine whether this association differs from that for postmenopausal IGF-l levels.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".