2D:4D digit ratio as a potential marker for prostate cancer risk
Bibliographic record
Abstract
BACKGROUND: The second-to-fourth digit ratio (2D:4D) is thought to reflect prenatal exposure to sex steroids. We investigated the relationship between 2D:4D and odds of prostate cancer. METHOD: Data were collected in PROtEuS, a population-based case-control study conducted in Montréal, Canada (2005-2012), including 1931 incident prostate cancer cases aged < 76 years and 1994 population controls. In-person interviews elicited information on potential risk factors. Digit lengths were measured by interviewers applying a standard protocol. Odds ratios (OR) and 95 % confidence intervals (CI) were estimated using unconditional logistic regression adjusting for potential confounders. RESULTS: The OR of prostate cancer for a standard deviation increase in 2D:4D was 0.91 (95 % CI: 0.85-0.98). For less and more aggressive cancers, ORs were 0.93 (95 % CI: 0.87-1.00) and 0.85 (95 % CI: 0.77-0.93), respectively. There was an interaction with ancestry (p=0.04), whereas the OR among men of African descent was 1.23 (95 % CI: 0.96-1.57, based on 128 cases). CONCLUSION: Findings suggest an inverse association between 2D:4D and odds of overall prostate cancer, more pronounced for aggressive cancers. This supports the notion that high levels of testosterone in utero, estimated by a low 2D:4D ratio, are associated with a higher risk of prostate cancer. Contrastingly, a high digit ratio was associated with greater cancer odds among participants of African descent. Upon replication, 2D:4D could prove to be an easily measured marker of prostate 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".