A sequence repeat in the insulin‐like growth factor‐1 gene and risk of breast cancer
Bibliographic record
Abstract
Insulin-like growth factor-1 (IGF-I), a potent mitogen, is hypothesized to influence breast cancer risk. In 3 previous studies, a polymorphism in the IGF-1 gene (sequence repeat length) was associated with plasma IGF-I level. We evaluated prospectively the relationships among a (CA)(n) repeat polymorphism in the IGF-1 gene, IGF-I level and breast cancer risk in a nested case-control study conducted within the Nurses' Health Study. Blood samples were collected in 1989-1990; up to June 1994, we identified 463 cases of breast cancer. One to 2 controls were selected per case, matched by age, menopausal status, postmenopausal hormone use, month and time of day of blood collection and fasting status, for a total of 622 controls. Although no significant trend was observed, plasma IGF-I levels were significantly lower among controls, with no copy of the 19 allele, compared with those homozygous for the 19 (CA)(n) repeat length (146 and 173 ng/ml, respectively; p-value for pairwise mean comparison = 0.005). In conditional logistic regression, controlling for established breast cancer risk factors, we observed no significant association between (CA)(n) repeat length genotype and risk of breast cancer [compared with repeat genotype 19/19-18/19 genotype relative risk (RR) = 0.96, 95% confidence interval (CI) = 0.56-1.64; 18/20 genotype RR = 0.92, 95% CI = 0.39-2.19; 19/20 genotype RR = 1.16, 95% CI = 0.82-1.64; 19/21 genotype RR = 0.69, 95% CI = 0.42-1.14; 20/20 genotype RR = 0.55, 95% CI = 0.28-1.10; 20/21 genotype RR = 0.72, 95% CI = 0.29-1.79]. Results did not vary substantially when evaluated according to menopausal status, tumor receptor status or category of other breast cancer risk factors. Although a modest association cannot be excluded, our data do not support an important relation between this IGF-1 gene polymorphism and breast cancer risk.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".