Advances in the management of high‐risk localised and metastatic prostate cancer
Bibliographic record
Abstract
At the third annual Interactive Genitourinary Cancer Conference, held in Budapest from 30 April to 1 May 2011, the latest developments in the management of patients with high-risk localised and metastatic prostate cancer were discussed. Prostate cancer is the most common cancer in Western men and, for advanced disease, no curative agents are available. For men with high-risk localised disease there is debate about the best treatment approaches, with both radical prostatectomy and radiation therapy shown to improve outcomes. These approaches have started to be augmented as new techniques and therapies are developed. For instance, radiation therapy combined with androgen deprivation therapy has been shown to be more efficacious than radiation therapy alone, and there may also be a role for adjuvant/neoadjuvant chemotherapy. Ultimately a multidisciplinary approach will most probably result in the best outcomes for patients. The use of androgen deprivation therapy in men with prostate cancer needs to be monitored carefully, given that it results in adverse alterations in several metabolic parameters and an increased risk of further coronary events in men with cardiovascular disease in some studies. Until recently there were limited options for the management of men with advanced prostate cancer, but new agents for use in the post-docetaxel setting have recently been approved. These are cabazitaxel and abiraterone acetate, which have both shown a significant survival benefit in patients who have progressed on docetaxel. Additional agents, for these patients and for patients at other stages of disease, are in the later stages of development. The development of new agents has been aided by a greater understanding of the molecular mechanisms of resistance to current therapies and the recognition of new pathophysiological pathways. As the number of available therapeutic options increases, it will become increasingly important to tailor treatments to the individual patient. This may require the development of novel biomarkers or the use of existing or new predictive tools based on prognostic factors. To ensure optimal patient care, early and continuous involvement of the multidisciplinary team will be required.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".