Optimizing Clinical Implementation of Hypofractionation: Comprehensive Evidence Synthesis and Practical Guidelines for Low- and Middle-Income Settings
Bibliographic record
Abstract
The global cancer burden, especially in low- and middle-income countries (LMICs), worsens existing disparities, amplified by the rising costs of advanced treatments. The shortage of radiation therapy (RT) services is a significant issue in LMICs. Extended conventional treatment regimens pose significant challenges, especially in resource-limited settings. Hypofractionated radiotherapy (HRT) and ultra-hypofractionated/stereotactic body radiation therapy (SBRT) offer promising alternatives by shortening treatment durations. This approach optimizes the utilization of radiotherapy machines, making them more effective in meeting the growing demand for cancer care. Adopting HRT/SBRT holds significant potential, especially in LMICs. This review provides the latest clinical evidence and guideline recommendations for the application of HRT/SBRT in the treatment of breast, prostate, and lung cancers. It emphasizes the critical importance of rigorous training, technology, stringent quality assurance, and safety protocols to ensure precise and secure treatments. Additionally, it addresses practical considerations for implementing these treatments in LMICs, highlighting the need for comprehensive support and collaboration to enhance patient access to advanced cancer care.
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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 | 0.046 | 0.133 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".