Hypofractionation Adoption in Prostate Cancer Radiotherapy: Results of an International Survey
Bibliographic record
Abstract
PURPOSE Hypofractionation is noninferior to conventional fractionation in the treatment of localized prostate cancer. Using results from the European Society of Radiation Oncology's (ESTRO) Global Impact of Radiotherapy in Oncology (GIRO) initiative survey on hypofractionation, this study identifies rates of adoption, facilitating factors, and barriers to adoption of hypofractionation in prostate cancer across World Bank income groups. MATERIALS AND METHODS The ESTRO-GIRO initiative administered an international, anonymous, electronic survey to radiation oncologists from 2018 to 2019. Physician demographics, clinical practice characteristics, and hypofractionation regimen use (if any) for several prostate cancer scenarios were collected. Responders were asked about specific justifications and barriers to adopting hypofractionation, and responses were stratified by World Bank income group. Multivariate logistic regression models were used to analyze variables associated with hypofractionation preference. RESULTS A total of 1,157 physician responses were included. Most respondents (60%) were from high-income countries (HICs). In the curative setting, hypofractionation was most often preferred in low- and intermediate-risk prostate cancers, with 52% and 47% of respondents reporting hypofractionation use in ≥50% of patients, respectively. These rates drop to 35% and 20% in high-risk prostate cancer and where pelvic irradiation is indicated. Most respondents (89%) preferred hypofractionation in the palliative setting. Overall, respondents from upper-middle-income countries and lower-middle- and low-income countries were significantly less likely to prefer hypofractionation than those from HICs ( P < .001). The most frequently cited justification and barrier were availability of published evidence and fear of worse late toxicity, respectively. CONCLUSION Hypofractionation preference varies by indication and World Bank income group, with greater acceptance among providers in HICs for all indications. These results provide a basis for targeted interventions to increase provider acceptance of this treatment modality.
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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".