Bibliographic record
Abstract
The prostate is a susceptible organ that could be impacted by metabolic diseases such as diabetes mellitus (DM), and more specifically type 2 DM (T2DM). Different literature sources suggest various associations between T2DM and prostatic diseases, most notably prostate cancer (PCa) and benign prostatic hyperplasia (BPH). This article serves as a comprehensive literature review on T2DM and anti-DM agents concerning PCa and BPH. Despite DM playing a role in contributing to the high risk of different malignancies and leading to worse mortality outcomes, multiple sources from the literature explain that DM might hold a protective effect against PCa. Several reasons could be discussed concerning such a lower risk, such as the dominant phenotype of T2DM patients, genomics, and T2DM's impact on testosterone and prostatic vasculature. It also appears that the risk is associated with the time of T2DM diagnosis. Still, such a notion is not universally established, as some studies show that pre-existing DM in patients diagnosed with PCa increases mortality risk. DM might produce obstructive and irritative symptoms that mimic those of BPH. Such symptoms could be more challenging to respond to typical BPH therapy if glycemic control was not optimal. The dominant literature sources suggest that T2DM, obesity, and metabolic syndrome increase the risk of BPH. Metformin appears to be the most prominent anti-DM agent in terms of its positive effect on inhibiting PCa cell growth. Multiple trials are still ongoing to discover more of its anti-malignant roles. Conflicting reports are still present regarding multiple anti-DM agents and their true impact on PCa and BPH. It is vital to recognize that a relationship exists between prostatic diseases and T2DM. Close follow-up and screening are needed from both ends of the clinical evaluation.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".