Hypofractionated Radiotherapy in Gynecologic Malignancies—A Peek into the Upcoming Evidence
Bibliographic record
Abstract
Radiotherapy (RT) has a fundamental role in the treatment of gynecologic malignancies, including cervical and uterine cancers. Hypofractionated RT has gained popularity in many cancer sites, boosted by technological advances in treatment delivery and image verification. Hypofractionated RT uptake was intensified during the COVID-19 pandemic and has the potential to improve universal access to radiotherapy worldwide, especially in low-resource settings. This review summarizes the rationale, the current challenges and investigation efforts, together with the recent developments associated with hypofractionated RT in gynecologic malignancies. A comprehensive search was undertaken using multiple databases and ongoing trial registries. In the definitive radiotherapy setting for cervical cancers, there are several ongoing clinical trials from Canada, Mexico, Iran, the Philippines and Thailand investigating the role of a moderate hypofractionated external beam RT regimen in the low-risk locally advanced population. Likewise, there are ongoing ultra and moderate hypofractionated RT trials in the uterine cancer setting. One Canadian prospective trial of stereotactic hypofractionated adjuvant RT for uterine cancer patients suggested a good tolerance to this treatment strategy in the acute setting, with a follow-up trial currently randomizing patients between conventional fractionation and the hypofractionated dose regimen delivered in the former trial. Although not yet ready for prime-time use, hypofractionated RT could be a potential solution to several challenges that limit access to and the utilization of radiotherapy for gynecologic cancer patients worldwide.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".