Principles of optimal multidisciplinary management of prostate cancer in clinical practice
Bibliographic record
Abstract
Advances in the diagnosis and management of prostate cancer have significantly changed the disease landscape. While benefiting from better oncological outcomes, patients are now experiencing higher rates of non-cancer comorbidities, including cardiovascular disease. The increasing impact of cardiovascular disease in those with prostate cancer led to the expanding role of cardio-oncology professionals in enhancing the multidisciplinary care of these patients. As a result, the International Cardio-Oncology Society (IC-OS) launched a 4-webinar series in collaboration with the European Association of Urology and the Canadian Urology Association to inform best practices in the multidisciplinary care of patients with prostate cancer. This program highlighted currently recommended diagnostic and treatment strategies from urology, oncology, and cardiology and emphasized knowledge gaps and future directions. In this article, which is the second in a 2-part series, we review challenging cases that were presented and discussed among a multidisciplinary international panel and highlight ongoing research and future directions from both urology/oncology and cardio-oncology.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".