Insulin-like growth factor-I, its binding proteins (IGFBP-1 and IGFBP-3), and growth hormone and breast cancer risk in The Nurses Health Study II
Bibliographic record
Abstract
Earlier data suggest that the relationship between circulating insulin-like growth factor I (IGF-I) levels and breast cancer risk differs according to menopausal status. We evaluated the association between IGF levels as well as the primary regulator of IGF-I production, growth hormone (GH), and breast cancer risk in the Nurses' Health Study II (NHS II) cohort, a large cohort of primarily premenopausal women. We conducted a case-control study nested within the prospective NHS II cohort. Plasma concentrations of IGF-I, IGF binding protein (IGFBP)-3, IGFBP-1, and GH were measured in blood samples collected between 1996 and 1999. Totally 317 women were identified who had a diagnosis of invasive or in situ breast cancer between the date of blood collection and June 1 2003; 75% of these women were premenopausal at blood collection. To each of the 317 women, two controls were age-matched for a total of 634 controls. We used conditional logistic regression models to estimate the relative risk of breast cancer. Overall, plasma IGF-I, IGFBP-1, IGFBP-3, and GH levels were not associated with breast cancer risk (relative risks, top vs bottom quartile; IGF-I, 0.98, 95% confidence interval (CI), 0.69-1.39; IGFBP-1, 0.95, 95% CI, 0.63-1.41; IGFBP-3, 1.10, 95% CI, 0.78-1.54; GH, 1.09, 95% CI, 0.82-1.46). These risks were similar for premenopausal women of age 45 years or less. Further adjustment for additional breast cancer risk factors did not change these estimates. In conclusion, circulating IGF-I, IGFBP-1, IGFBP-3, and GH levels appear to have no important association with breast cancer risk in a large cohort of premenopausal women.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".