Clinical Outcomes and Institutional Experience of Ultrahypofractionated Radiation Therapy in Patients With Breast Cancer
Bibliographic record
Abstract
Purpose: Prompted by COVID-19 and publication of the FAST-Forward study, our institution rapidly implemented ultrahypofractionated radiation therapy (U-HFRT) for patients with early-stage breast cancer. Our objective was to evaluate our early experience and toxicity outcomes for U-HFRT. Methods and Materials: Patients with consecutive stage 0-II breast cancer treated with adjuvant whole breast radiation therapy (RT) were evaluated. Patient demographics and treatment characteristics were extracted and categorized into 2 cohorts: U-HFRT, 26 Gy/5 fractions (F) and M (moderate)-HFRT, 40.05 Gy/15F. Physician-assessed skin toxicity was evaluated using the Radiation Therapy Oncology Group radiation morbidity scale at baseline/during RT, 1 to 90 days post-RT and >90 days post-RT. Descriptive statistics summarized patient demographics and treatment characteristics and were stratified based on dose fractionation. A multivariable logistic model evaluated associations between toxicity and fractionation. Results: < .014) after adjusting for boost, age, and chemotherapy. Rates of skin toxicity >90 days post-RT were low overall. Conclusions: This study reports real-world clinical outcomes of patients with stage 0-II breast cancer treated with U-HFRT. We observed low rates of acute skin toxicity compared to M-HFRT, confirming its acceptability as a standard regimen for select patients. Longer term follow-up would be necessary to confirm clinical outcomes in terms of both local control and late normal tissue toxicity.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".