The association of male pattern baldness and risk of cancer and high-grade disease among men presenting for prostate biopsy
Bibliographic record
Abstract
INTRODUCTION: Androgens have been implicated in both male pattern baldness (MPB) and prostate cancer (PCa). We set out to prospectively determine if men with independently assessed MPB are at higher risk for PCa at biopsy and determine if any grade associations exist. METHODS: We prospectively enrolled 394 eligible patients presenting for prostate biopsy and independently determined their MPB pattern using the validated modified Norwood classification system (0: no balding; 1: frontal balding; 2: mild vertex balding; 3: moderate vertex balding; 4: sever vertex balding). Univariate and multivariable models, including Norwood score, age, prostate-specific antigen, and digital rectal examination abnormalities, were calculated for the outcomes of cancer and high-grade disease (Gleason >6). C-statistics analyses of our models were then compared with and without MPB pattern for marginal utility. RESULTS: Norwood patterns were increasingly associated with cancer and high-grade disease with a dose-effect (p for trend <0.001 on univariate and multivariable analyses for cancer and p=0.001 and p=0.0036 for high-grade disease on univariate and multivariable analyses, respectively). On multivariable analyses, trends still held, with all patients exhibiting Norwood scale 3 and 4 at increased risk for cancer. In predicting risk of high-grade disease, only patients with Norwood pattern 4 exhibited an increased risk. CONCLUSIONS: MPB appears to be a strong and independent risk factor for both cancer and high-grade disease for men presenting for prostate biopsy. Ours could be superior to marketed costly genetic tests. Further research is needed to understand the biology behind this observation and to incorporate these findings into clinical decision-making.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".