Prostate cancer and androgenic alopecia: The role of finasteride as a dual agent
Bibliographic record
Abstract
Background: Prostate cancer (PC) is recognised as the leading cancer diagnosis in Canadian men and the third-highest cause of cancer mortality. Despite its prevalence increasing with age, in Canada, funding for PC research is much lower when compared to other forms of cancer. Androgenic alopecia (AA) is a medical condition known to affect more than a third of all men and women. Studies show that AA is a medical condition with significant physical and psychological harms to the patient but one that can be treated effectively by a general practitioner. Objective: The objective is to determine the possibility of using finasteride to concurrently treat AA and prevent PC for men with more than one PC risk factor, one of which being AA. Methods: Numerous databases were searched, including OVID, Cochrane, Medline and PubMed. Additional internet searches were done using keywords such as dihydrotestosterone (DHT), AA, male pattern baldness, prostate-specific antigen, PC and finasteride, including a Medical Subject Headings search combining these words. Results and Conclusion: As a risk indicator for a PC diagnosis, early recognition of AA is important. Considering AA in conjunction with other risk factors for PC provides a more accurate risk assessment for patients. Finasteride has been shown to be effective in treating AA. By reducing DHT, a determining factor of both AA and PC development, finasteride has been demonstrated to reduce the risk of PC development by 25%. Finasteride therapy is an effective, low-risk, AA treatment option with the benefit of protecting potentially more susceptible men from PC. Given its excellent safety profile, as demonstrated in long-term studies of benign prostatic hyperplasia treatment, finasteride should be offered routinely to men with AA.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".