Cabazitaxel for Metastatic Castrate-Resistant Prostate Cancer: A Case Presentation
Bibliographic record
Abstract
Introduction: Although cabazitaxel was proven efficacious by a large Phase III trial and was approved for second-line treatment of metastatic castrate-resistant prostate cancer, case reports describing its efficacy and safety are lacking in the literature. More data is needed describing castrate-resistant prostate cancer cases that progress with 1st line therapy. Case Presentation: A 78-year old Hispanic male presented to the clinic in July 2011 with a 3-month history of worsening left knee pain, generalized fatigue, and a 5.4 kilogram weight loss. His past medical history was significant for metastatic prostate cancer and his laboratory results were notable for an alkaline phosphatase of 221U/L, a prostate specific antigen of 837.7ng/ml, and a creatinine of 1.94. Discussion: Metastatic prostate cancer results from the combination of lymphatic, blood, or contiguous local spread. Cabazitaxel is a novel semi-synthetic tubulin binding taxane that uses a precursor molecule extracted from yew tree needles. In the phase III TROPIC study, CRPC patients previously taking docetaxel had a significant increase in overall survival with cabazitaxel compared with mitoxantrone (15.1 vs. 12.7 months, p<0.001). Conclusion: Cabazitaxel appears to be safe chemotherapeutic agent and has moved to the forefront in our armamentarium for the treatment in castrate-resistant prostate cancer.
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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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| 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".