Prognostic and predictive clinical factors in patients with metastatic castration-resistant prostate cancer treated with cabazitaxel
Bibliographic record
Abstract
INTRODUCTION: Cabazitaxel is one of several treatment options available for patients with metastatic castration-resistant prostate cancer who have progressed on docetaxel. Little is known about clinical factors that influence prognosis or treatment response for patients receiving cabazitaxel. Identifying prognostic and predictive factors could contribute to the optimal selection of patients for treatment after docetaxel. METHODS: A retrospective review of patients enrolled on the cabazitaxel Canadian Early Access Program (C-EAP) was performed. Clinical factors were analyzed by univariable and multivariable Cox proportional hazards and logistic regression analysis to identify independent predictors of prognosis and response. RESULTS: Forty-five patients from five centres in Canada were included in this study. On multivariable analysis, lower hemoglobin was associated with shorter survival. No other factors were independently associated with survival, prostate-specific antigen (PSA) response, or primary PSA progression. CONCLUSIONS: Clinical factors predicting survival or treatment response were not identified for men with castration-resistant prostate cancer receiving cabazitaxel. Larger studies may be necessary to identify clinical factors and biomarkers that identify whether patients should or should not receive cabazitaxel.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".