Clinical outcome of Taiwanese men with clinically localized prostate cancer post-radical prostatectomy: a comparison with other ethnic groups
Bibliographic record
Abstract
BACKGROUND: Prostate cancer incidence varies significantly among different ethnic groups. However, the report concerning the clinical outcome after radical prostatectomy (RP) in the low incidence Asian population is still limited. We aimed to compare the clinical outcome in patient treated with RP among different ethnic groups and to identify significant prognostic factors in Taiwanese patients. METHODS: A total of 341 patients with clinical localized prostate cancer undergoing curative RP in three medical centers in Taiwan were included in this study. Ethnic group comparison was performed using the CaPSURE, SEARCH databases from United States (US) and one large European series. The Kaplan-Meier analysis and Cox proportional hazard model were used to identify significant predictors for prostate-specific antigen (PSA) recurrence. RESULTS: Compared to the Caucasian white population in the US and Europe studies, the Taiwanese population have higher age at surgery and higher pre-operative PSA level. With mean and median follow-up of 39.1 months and 31.0 months (range 5-120 months), 127 men (37.2%) had PSA recurrence which was significant higher than the Western series. Significant predictors for PSA recurrence identified in the post-operative overall model were PSA level, pathological Gleason Score, pathological tumor stage and lymph node metastasis. CONCLUSIONS: The clinical outcome of Taiwanese male with prostate cancer post-RP appears inferior to the Western country, which is largely due to delay surgery at higher PSA level. Earlier diagnosis and treatment may improve the cancer control of RP.
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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 | 0.001 | 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".