Survival Rate and Prognostic Factors of Localised Prostate Cancer in Southeast Asian Countries: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Prostate cancer (Pca) is one of the most prevalent health conditions affecting men, particularly older men, and cases have increased in recent years. OBJECTIVE: This review examined the survival rate and prognostic factors of patients with Pca in Southeast Asia (SEA). METHODS: We conducted a systematic search of three databases (PubMed, Scopus, Web of Science) and a manual search until April 1, 2022. The selected papers were evaluated using the Newcastle-Ottawa Quality Assessment Form for Cohort Studies. The review protocol was registered with PROSPERO (CRD42022326521). Pooled prevalence rates were calculated using the programme R version 4.2.1. Heterogeneity was assessed using the I2 statistic and p-value. A narrative approach was used to describe prognostic factors. Studies were selected and finalised based on the review question. The quality of the included studies was assessed. RESULTS: A total of 11 studies were included in this review. The 1-, 3-, 5- and 10-year survival rates of SEA Pca cases were 80.8%, 51.9%, 66.1% (range 32.1-100) and 78% (range 55.9-100), respectively. Prognostic factors for Pca were discussed in terms of sociodemographic, disease-related and treatment-related aspects. The predictors of significantly lower survival were age more than 75 years, cancer detected during transurethral resection of the prostate, Gleason score more or equal to eight, high-risk group, metastases and no adjuvant radiotherapy. A meta-analysis on the pooled HR of prostate cancer could not be performed due to the heterogeneity of prognostic factors. The pooled prevalence of localised and metastatic prostate cancer in SEA countries was 39% 95% CI [20-62] and 40% 95% CI [28-53], respectively. CONCLUSION: The survival rate in SEA countries can be determined by prognostic factors, which can be divided into sociodemographic, disease-related and treatment-related factors. Therefore, further studies are needed to improve the understanding and treatment of Pca in the region SEA.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".