Relationship Between Pre- and Post-Orchidectomy Serum Dihydrotestosterone and Prostate Cancer Severity in a Cohort of Nigerian Patients.
Bibliographic record
Abstract
INTRODUCTION: Prostate cancer (PCa) is the commonest urologic cancer worldwide and the leading cause of male cancer deaths in Nigeria. In Nigeria, orchidectomy remains the primary androgen deprivation therapy. Dihydrotestosterone (DHT) is the active prostatic androgen, but its relationship with PCa severity has not been extensively studied in Africa. OBJECTIVES: This study assessed the relationship between serum levels of DHT (pre- and post-orchidectomy) and serum prostate specific antigen (PSA), as well as Gleason scores. METHODS: Patients undergoing orchidectomy for histologically confirmed prostate adenocarcinoma were studied. Serum PSA and DHT levels were assessed before orchidectomy, and 6 weeks afterwards. This was correlated with their Gleason scores. Data was analyzed using the IBM SPSS Statistics version 20. P < 0.05 was considered significant. RESULTS: Fifty-three patients completed the study. The mean age was 69.3 ± 6.9 years. Pre- and post-orchidectomy serum PSA ranged from 11.30 to 562.00 ng/ml and 0.01ng/mL to 245.00 ng/mL respectively. Pre- and post-orchidectomy DHT ranged from 6.91ng/mL to 4,996.38 ng/mL and 6.56 ng/mL to 2,575.03 ng/mL respectively. Up to 40% still had normal DHT post-orchidectomy. There was a positive but statistically insignificant correlation between pre-orchidectomy serum PSA and DHT (r = 0.089, p = 0.527). There was however a direct and significant relationship between pre-orchidectomy serum DHT and Gleason scores (p = 0.042). CONCLUSION: This study showed a relationship between preorchidectomy serum DHT and Gleason scores. Assessing DHT in patients with high Gleason scores could influence hormonal manipulation.
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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 | 0.001 | 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) | 0.002 | 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".