The third symposium on treatment-induced neuroendocrine prostate cancer: insights and future directions
Bibliographic record
Abstract
Neuroendocrine prostate cancer (NEPC) is a rare and aggressive subtype of prostate cancer (PCa), emerging from advanced treatments and characterized by loss of androgen receptor (AR) signaling and neuroendocrine features, leading to rapid progression and treatment resistance. The third symposium on treatment-induced NEPC, held from 21 to 23 June 2024, at Harrison Hot Springs Resort, BC, Canada, united leading global researchers and clinicians. Sponsored by the Vancouver Prostate Centre (VPC), Canadian Institute of Health Research, Prostate Cancer Foundation Canada and Pharma Planter Inc, the event focused on the latest NEPC research and innovative treatment strategies. Co-chaired by Drs. Yuzhuo Wang and Martin Gleave, the symposium featured sessions on NEPC's historical context, molecular pathways, epigenetic regulation and the role of the tumor microenvironment and metabolism in its progression. Keynotes from experts like Dr. Himisha Beltran and Dr. Martin Gleave highlighted the complexity of NEPC. The Emerging Talent session showcased new research, pointing to the future of NEPC treatment. The symposium concluded with a consensus on the need for early detection, targeted therapies and personalized medicine to effectively combat NEPC, emphasizing the importance of global collaboration in advancing NEPC understanding and treatment.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".