Management of advanced prostate cancer in the Asia‐Pacific region: Summary of the Asia‐Pacific Advanced Prostate Cancer Consensus Conference 2023
Bibliographic record
Abstract
AIM: The aim of the third Asia-Pacific Advanced Prostate Cancer Consensus Conference (APAC APCCC 2023) was to discuss the application in the Asia-Pacific (APAC) region of consensus statements from the 4th Advanced Prostate Cancer Consensus Conference (APCCC 2022). METHODS: The one-day meeting in July 2023 brought together 27 experts from 14 APAC countries. The meeting covered five topics: (1) Intermediate- and high-risk and locally advanced prostate cancer; (2) Management of newly diagnosed metastatic hormone-sensitive prostate cancer; (3) Management of non-metastatic castration-resistant prostate cancer; (4) Homologous recombination repair mutation testing; (5) Management of metastatic castration-resistant prostate cancer. Pre- and post-symposium polling gathered APAC-specific responses to APCCC consensus questions and insights on current practices and challenges in the APAC region. RESULTS: APAC APCCC highlights APAC-specific considerations in an evolving landscape of diagnostic technologies and treatment innovations for advanced prostate cancer. While new technologies are available in the region, cost and reimbursement continue to influence practice significantly. Individual patient considerations, including the impact of chemophobia on Asian patients, also influence decision-making. CONCLUSION: The use of next-generation imaging, genetic testing, and new treatment combinations is increasing the complexity and duration of prostate cancer management. Familiarity with new diagnostic and treatment options is growing in the APAC region. Insights highlight the continued importance of a multidisciplinary approach that includes nuclear medicine, genetic counseling, and quality-of-life expertise. The APAC APCCC meeting provides an important opportunity to share practice and identify APAC-specific issues and considerations in areas of low evidence where clinical experience is growing.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".