Reply to neoadjuvant therapy followed by prostatectomy for clinically localized prostate cancer
Bibliographic record
Abstract
Drs. Koontz and Moul provide a sound rationale supporting the study of neoadjuvant radiotherapy (RT) before radical prostatectomy (RP) to improve outcomes. Several trials have focused on the employment of systemic agents to address the issue of microscopic metastases in this setting. However, recent data indicate that local recurrence rates may be significantly higher than first appreciated, which could potentially lead to lethal distant metastases. Nearly half of patients with a prostate-specific antigen (PSA) recurrence after RP have a long-term PSA response to salvage RT when treatment is administered at the earliest sign of disease recurrence.1 In 2 randomized trials, significant improvements in PSA and local recurrence rates were noted with the addition of adjuvant RT after RP in patients with high-risk disease.2, 3 In the setting of locally advanced rectal cancer, which has a somewhat similar pattern of both local and distant disease recurrences, trimodality therapy with neoadjuvant chemoradiotherapy followed by surgery has led to improved long-term outcomes. In addition, neoadjuvant RT for rectal cancer appeared to be less toxic and more effective in reducing local recurrences when compared with adjuvant RT.4 The advantages of neoadjuvant RT may include improved compliance as well as downstaging, which may enhance the rate of curative surgery. In addition, because tumor oxygenation is better in the preoperative setting, neoadjuvant RT may be more effective. Thus, strategies that focus on improved local control, including neoadjuvant RT, may contribute to the reduction in local as well as, eventually, distant disease recurrences. The separate ongoing trials of neoadjuvant RT or concurrent RT plus docetaxel that are currently underway at Duke University and Oregon Health and Science University, respectively, are important steps in the development of this strategy. Guru Sonpavde MD* , Mark Garzotto MD , Kim N. Chi MD?, * Genitourinary Oncology Program, Texas Oncology and, U.S. Oncology Research, Houston, Texas, Scott Department of Urology, Baylor College of Medicine, Houston, Texas, Department of Urology, Oregon Health and Science University, Portland, Oregon, ? British Columbia Cancer Agency, Vancouver Cancer Center, Vancouver, British Columbia, Canada.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.028 | 0.031 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".