Data from Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer
Bibliographic record
Abstract
AbstractBackground: The link between hormones and hair growth is well established. Inconsistent associations have been found between hair patterns and cancer of the prostate, a hormone-dependent organ. We assessed vertex baldness trajectories, chest hair amount, and their relationships with the odds of developing prostate cancer in a large case–control study in Montreal, Canada. Methods: In-person interviews were conducted with 1,931 incident prostate cancer cases and 1,994 population-based age-matched (±5 years) controls. Participants reported their hair patterns using the validated Hamilton–Norwood scale of baldness for 10-year increments starting at age 30, and their current amount of chest hair. Group-based trajectories were used to identify men sharing similar patterns of vertex baldness severity over adulthood. Multivariable logistic regression assessed associations between indicators of baldness (frontal, vertex, age at onset, severity, and trajectories), chest hair, and odds of prostate cancer. Results: Vertex balding onset at age 30 was associated with increased odds of overall prostate cancer [Odds ratio (OR), 1.30; 95% confidence interval (CI), 1.03–1.64]. Men in the trajectory characterized by early moderate vertex baldness and developing severe baldness had increased odds of overall (OR, 1.42; 95% CI, 1.03–1.96) and especially aggressive prostate cancer (OR, 1.98; 95% CI, 1.21–3.22) compared with men without baldness. Men with little chest hair had higher odds of aggressive tumors than those with a moderate amount/a lot of chest hair. Conclusions: Early-onset moderate vertex baldness that progresses and having little chest hair may be useful biomarkers of aggressive prostate cancer. Impact: Integration of early-onset vertex balding patterns into risk prediction models of aggressive prostate cancer should be envisaged.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".