Androgens, growth factors, and risk of prostate cancer: The Multiethnic Cohort
Bibliographic record
Abstract
BACKGROUND: Androgens and growth factors are thought to be associated with prostate cancer risk, although past research has produced mixed results. METHODS: We conducted a nested case-control study of biomarkers of prostate cancer risk within the Multiethnic Cohort. We compared prediagnostic levels of testosterone, dihydrotestosterone (DHT), sex hormone-binding globulin (SHBG), 3alpha-androstanediol glucuronide (3alpha-diol G), insulin-like growth factor I (IGF-I), IGF-II, IGF-binding protein 1 (IGFBP-1), and IGFBP-3 in serum from 467 incident prostate cancer cases and 934 cancer-free controls. Controls were matched to the cases on geographic site (HI, LA), ethnicity, age at specimen collection (+/-1 year), date (+/-1 month) and time of day (+/-2 hr) of sample collection, and fasting status (<6, 6-7, 8-9, >10 hr). Multivariate conditional logistic regression models were used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Serum concentrations of testosterone, DHT, SHBG, 3alpha-diol G, IGF-I, IGF-II, IGFBP-1, and IGFBP-3 were not associated with risk of prostate cancer. Tests for trend of quartiles of serum concentrations also did not show any association. Results were relatively unchanged for men with advanced prostate cancer and their matched controls. However, the follow-up period was relatively short (mean of 1.9 years). Analysis by ethnic group showed an increased risk for Latino men in the second (OR = 3.67, 95% CI: 1.63-8.24) and third (OR = 2.96, 95% CI: 1.19-7.40) tertiles of IGF-I serum levels compared with the first tertile. CONCLUSIONS: The suggested increased risk for IGF-I in Latino men merits further study, with greater statistical power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".