Hypofractionated Postoperative Radiation Therapy for Breast Cancer – Do We Need More Evidence or Adapted Reimbursement Policies?
Bibliographic record
Abstract
"More than 10 years ago, two pivotal trials, the Ontario Clinical Oncology Group trial and START B trial, firmly established that hypofractionated radiation therapy (RT) of 40–42.5 Gy in 15–16 fractions over 3 weeks after breast conserving surgery or mastectomy results in similar rates of local recurrence and normal tissue effects. This led to a new standard for postoperative whole breast and chest wall RT. Further trials confirmed these findings and show that hypofractionated RT can also be applied for other indications, including regional nodal RT and for ductal carcinoma in situ (DCIS). More recently, a so-called ultra-fractionation trial demonstrated that 26 Gy in 5 fractions over 1 week was non-inferior to 40 Gy, in 15 fractions in 3 weeks for local recurrence at 6 years and that late effects were similar between fractionation schedules being a treatment option for most patients with early breast cancer. Several countries and departments are now adopting hypofractionated schedules as a new standard for breast, chest wall or partial breast RT. In addition to the improvement in convenience and reduction in resources required, hypofractionated RT offers important benefits with respect to acute and late toxicity that can improve the quality of life of patients receiving breast RT."
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 | 0.068 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.014 |
| 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".