Society of Urologic Oncology position statement: Redefining the management of hormone‐refractory prostate carcinoma
Bibliographic record
Abstract
Because patients with hormone-refractory prostate carcinoma are a very diverse group, management of these patients represents a unique challenge. Despite much research, to the authors' knowledge few studies published to date have provided definitive treatment answers. The Society of Urologic Oncology (SUO) convened a multidisciplinary panel of urologists, oncologists, and radiation oncologists to develop a treatment algorithm for patients with hormone-refractory prostate carcinoma. The resulting treatment outline was based on a review of the literature review and on the expert opinions of the panelists. The current article provided a logical progression of treatment choices that included hormonal manipulations, chemotherapeutic options, and adjunctive therapies. Future clinical trials and therapies were also discussed by the authors. Management strategies should be targeted toward the individual patient. Although significant progress has been made in understanding and treating hormone-refractory prostate carcinoma, earlier interventions would be ideal and better therapeutic approaches to prolong survival are necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".