Hypofractionation for prostate cancer: an update
Bibliographic record
Abstract
INTRODUCTION: Recent advances in image guided radiation therapy (IGRT) has prompted much interest in the use of high-dose-per-fraction regimens for prostate cancer. Furthermore, from a radiobiological standpoint, there is increasing evidence that prostate tumors have a relatively low ɑ/β ratio therefore, the use of hypofractionation may potentially offer acceptable tumor control while minimizing late toxicity to critical structures. Areas covered: This expert review explores the current evidence regarding the safety and efficacy of hypofractionated radiotherapy for prostate cancer. A particular emphasis was placed on large, randomized phase III trials as these are most likely to influence clinical practice. The authors discuss the use of both moderate and extreme hypofractionation. Expert commentary: The recent publication of 5-year outcomes from large prospective trials of moderate hypofractionation enhances our confidence that these techniques are both safe and effective. We recommend the fractionation scheme of 60 Gy in 20 fractions as this regimen was not associated with any notable increase in late toxicity. With respect to extreme hypofractionation, mature phase III trials are needed to demonstrate the safety and efficacy of these techniques. For now, the use of radiosurgery should be limited to participation in prospective clinical trials.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".