Partial Breast Irradiation
Bibliographic record
Abstract
Breast cancer is the most common cancer in women worldwide. Over the past few decades, remarkable progress has been made in understanding the biology and pathology of breast cancer. A personalized conservative approach has been currently adopted addressing the patient's individual risk of relapse. After postoperative whole breast irradiation for early-stage breast cancer, a rate of recurrences outside the initial tumour bed lower than 4% was observed. Thus, the highest benefits of breast irradiation seem to result from the dose delivered to the tissue neighbouring the tumour bed. Nonetheless, reducing treatment morbidity while maintaining radiation therapy's ability to decrease local recurrences is an important challenge in treating patients with radiation therapy. In this regard, strategies such as partial-breast irradiation have been developed to reduce toxicity without compromising oncologic outcomes. According to the national and international published guidelines, clinical oncologists can refer to specific dose/fractionation schedules and eligible criteria. However, there are still some areas of open questions. Breast cancer represents a multidisciplinary paradigm; it should be considered a heterogeneous disease where a "one-treatment-fits-all" approach cannot be considered an appropriate option. This is a wide overview on the main partial breast irradiation advantages, risks, timings, techniques, and available recommendations. We aim to provide practical findings to support clinical decision-making, exploring future perspectives, towards a balance for optimisation of breast cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".