Androgen receptor gene polymorphism and breast cancer susceptibility in The Philippines.
Bibliographic record
Abstract
Up to one-third of women with breast cancer have a family history of breast cancer, and approximately 5% of cases are attributed to mutations in high-risk breast cancer susceptibility genes, such as BRCA1 and BRCA2. It is believed that genes of lower penetrance, but of greater prevalence, may also modulate a woman's risk of breast cancer. We studied the association of breast cancer and the trinucleotide repeat polymorphism (CAGn) in exon 1 of the androgen receptor gene (AR) in 299 cases of breast cancer and in 229 hospital-based controls from The Philippines. Women for whom the mean length of the CAG repeat allele was < or = 25 units had approximately one-half of the risk of breast cancer compared with women with a mean repeat length of > or = 26 [odds ratio (OR), 0.47; 95% confidence interval (CI), 0.28-0.8). The association with breast cancer risk was particularly strong among older women (> or = 50 years; OR, 0.2; 95% CI, 0.04-0.94). The association was also observed for the longer of the two AR alleles; there was a 5% increase in breast cancer risk for each unit increase in CAG repeat number. These findings support the theory that short trinucleotide repeat genotypes of the AR gene protect against breast cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".