Symptom assessment to guide treatment selection and determine progression in metastatic castration-resistant prostate cancer: Expert opinion and review of the evidence
Bibliographic record
Abstract
Multiple new agents to treat metastatic castration-resistant prostate cancer (mCRPC) have become available in recent years; however, the appropriate timing and sequencing of these agents have yet to be elucidated. Until accurate biomarkers become available to allow more focused therapeutic targeting for this population, treatment selection for men with mCRPC will continue to be driven largely by close assessment of patient-related factors and symptoms. Pain, as the predominant symptom of mCRPC, is often the focus when assessing progression and the need for a change in treatment. A myriad of other symptoms, including fatigue, impact on activities of daily living, sleep, and lower urinary tract symptoms, also affect men with mCRPC, and assessment of the composite of these symptoms provides an earlier signal for the need to adjust treatment. A number of tools are available for assessing symptoms in patients with advanced prostate cancer, but they are not routinely used, given their complexity and length. A new simplified questionnaire is proposed for the assessment of symptoms, beyond pain, to inform treatment decisions for men with mCRPC.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".