Gaps between Evidence and Practice in Postoperative Radiotherapy for Prostate Cancer: Focus on Toxicities and the Effects on Health-Related Quality of Life
Bibliographic record
Abstract
Adjuvant radiotherapy (ART) after prostatectomy for patients with high-risk features [extracapsular extension (ECE), seminal vesicle invasion (SVI), and positive margin] has been shown to be associated with improved biochemical disease-free survival in three large randomized trials and with improved overall survival in one. Similarly, salvage radiotherapy (SRT) can effectively achieve biochemical control in a significant proportion of patients with a rising PSA after surgery. Nonetheless, both approaches of postoperative RT remain highly underutilized. This might be partly due to concerns with overtreatment inherent to adjuvant approaches, and/or hesitance about causing radiation toxicities and their subsequent effects on the patient's quality of life. Herein, we review the literature lending evidence to these arguments. We show recent series of ART/SRT and their low rates of acute and long-term toxicities, translating only in transient decline in quality-of-life (QoL) outcomes. We conclude that concerns with side effects should not preclude the recommendation of an effective and curative-intent therapy for men with prostate cancer initially treated with radical surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".