Comparison of serum testosterone levels in prostate cancer patients receiving LHRH agonist therapy with or without the removal of the prostate
Bibliographic record
Abstract
INTRODUCTION: : The prostate secretes enzymes and nutrients to promote sperm motility. Recent reports suggest that the prostate may also secrete testosterone, which is believed to be a fuel for prostate tumour growth. The aim of this study was to determine if a difference in serum testosterone levels exists between men on luteinizing hormone releasing-hormone (LHRH) agonists who have undergone radical prostatectomy, radiation or hormone therapy as primary prostate cancer treatment. METHODS: : Serum testosterone levels were evaluated in 165 consecutive prostate cancer patients using LHRH analogues for >3 months. We excluded patients receiving either radiation or chemotherapy at time of time of testosterone measurement. Patients were classified based on primary treatment: (1) radical prostatectomy; (2) radiation; or (3) primary hormone therapy. We used one-way ANOVA to compare testosterone levels. Pearson correlation was used to correlate testosterone with prostate-specific antigen (PSA) and time on LHRH agonists. Multivariable linear regression was used to predict serum testosterone levels. RESULTS: : The median (interquartile range) serum testosterone levels were 1.4 (1-1.9), 1.3 (1-1.625) and 1.25 (0.9-1.525) nmol/L for radical prostatectomy, radiation and primary hormone therapy groups, respectively. There was no statistically significant difference in testosterone levels between the groups (p = 0.3). No correlation was found between testosterone and PSA levels or time on LHRH (r = 0.02 and r = 0.01), respectively. Multivariable linear regression showed that none of the clinical variables were predictors of serum testosterone levels. CONCLUSION: : Our study suggests that primary treatment does not affect serum testosterone levels among men using LHRH analogues.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".