Hypofractionation in Breast Cancer Radiotherapy Across World Bank Income Groups: Results of an International Survey
Bibliographic record
Abstract
PURPOSE: Hypofractionated breast radiotherapy has been found to be equivalent to conventional fractionation in many clinical trials. Using data from the European Society for Radiotherapy and Oncology Global Impact of Radiotherapy in Oncology survey, we identified preferences for hypofractionation in breast cancer across World Bank income groups and the perceived facilitators and barriers to its use. MATERIALS AND METHODS: An international, electronic survey was administered to radiation oncologists from 2018 to 2019. Demographics, practice characteristics, preferred hypofractionation regimen for specific breast cancer scenarios, and facilitators and barriers to hypofractionation were reported and stratified by World Bank income groups. Variables associated with hypofractionation were assessed using multivariate logistic regression models. RESULTS: < .001). Curative doses of 2.1 to < 2.5 Gy in 15-16 fractions were most frequently reported, with limited preference for ultra-hypofractionation, but significant variability in palliative dosing. In adjusted analyses, UMICs were significantly less likely than LLMICs to prefer hypofractionation across all curative clinical scenarios, whereas respondents with > 1 million population catchments and with intensity-modulated radiotherapy were more likely to prefer hypofractionation. The most frequently cited facilitators and barriers were published evidence and fear of late toxicity, respectively. CONCLUSION: Preference for hypofractionation varied for curative indications, with greater acceptance in earlier-stage disease in HICs and in later-stage disease in LLMICs. Targeted educational interventions and greater inclusivity in radiation oncology clinical trials may support greater uptake.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".