Treatment-Related Cognitive Impairment in Patients with Prostate Cancer: Patients’ Real-World Insights for Optimizing Outcomes
Bibliographic record
Abstract
Cognitive impairment (CI) is an issue that needs to be at the forefront of unmet healthcare needs in patients with prostate cancer (PCa) as it can negatively impact quality of life during long-term care. CI in patients with prostate cancer is thought to be influenced by treatment, androgen deprivation therapy (ADT), and novel androgen receptor (AR) pathway inhibitors in particular; however, current understanding is limited on how treatment affects cognition. Additionally, the experience of patients with CI who are receiving PCa treatment is not well understood or represented in clinical literature, which is a barrier to optimal patient outcomes in managing prostate cancer treatment-related cognitive impairment (PCa-TRCI). To help understand the patient journey and elucidate management gaps in PCa-TRCI, an international roundtable of healthcare provider and patient panelists was convened. The panelists focused on four key topic areas: (1) the patient experience when afflicted with, or at risk of, PCa-TRCI, (2) the physical, emotional, and social impact of CI on patients' quality of life (QoL), (3) the challenges that patients with PCa-TRCI face, and their impact on clinical decision-making, and (4) ways in which managing PCa-TRCI should evolve to improve patient outcomes. The purpose of the roundtable was to include patients in a direct discussion with healthcare providers (HCPs) regarding the patient journey and highlight real-world evidence of areas where patient outcomes could be improved in the absence of clinical evidence. The resulting discussion highlighted important healthcare gaps for patients with, and at risk of, PCa-TRCI and offered potential solutions as a roadmap to effective medicine.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".