Prognostic impact of serum testosterone in metastatic hormone-naive prostate cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE OF REVIEW: In daily practice, there is an unmet medical need for biomarkers that facilitate therapeutic decision-making in the metastatic hormone sensitive prostate cancer (mHSPC) scenario. Although recent studies have highlighted the potential of testosterone as a prognostic and predictive marker in prostate cancer, the evidence is controversial. The objective of this review was to summarize and analyze the scientific evidence regarding the prognostic role of basal testosterone levels in patients with mHSPC. METHODS: A systematic review was performed. Three authors selected the articles from Web of Science, PubMed, Scopus, and Cochrane Library electronic databases. Risk of bias was assessed by the Newcastle Ottawa Scale. RECENT FINDINGS: Most of the selected articles suggest that low testosterone levels before starting hormonal blockade imply a worse prognosis for patients with mHSPC. However, the quality of the evidence is poor, the studies are heterogeneous, and it is not possible to meta-analyze most of the published results. SUMMARY: Testosterone is an accessible and affordable biomarker. If it were correctly demonstrated that it harbors a prognostic and/or predictive role in the mHSPC setting, it could represent an advance in decision-making in these patients. Well designed prospective studies are needed to correctly answer this question.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.021 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".