High-Risk Localized Prostate Cancer: A Case for Early Chemotherapy
Bibliographic record
Abstract
High-risk tumors exhibit a more aggressive natural history and have higher positive margin and recurrence rates after radical prostatectomy or radiotherapy alone, where unimodality therapy likely represents undertreatment. Hence, the therapeutic ratio (patients who actually realize a survival benefit from a therapeutic intervention) may, therefore, be greater in high-risk disease if its natural history can be altered by multimodality therapy. It is, thus, important to investigate therapies that optimize complete extirpation of all cancer cells and reduce the incidence of positive surgical margins and disease recurrence. Neoadjuvant therapy extends the logic of early adjuvant therapy further by applying systemic therapy earlier in the course of the disease before definitive locoregional therapy. In prostate cancer, outcomes have not been improved significantly when neoadjuvant hormone therapy is used before surgery; although outcomes are improved when androgen ablation is combined with radiotherapy, many patients remain at risk for systemic recurrence. With recent data confirming improved survival data with docetaxel chemotherapy in metastatic disease, future trials are now focusing on earlier combinations of chemohormonal or biologic therapies in high-risk patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".