Preoperative Radio(Chemo)Therapy in Breast Cancer: Time to Switch the Perspective?
Bibliographic record
Abstract
AIM: Radiation therapy represents, together with surgery and systemic treatment, the triad on which the current management of patients with breast cancer is based, achieving high control and survival rates. In recent years we have witnessed a (r)evolution in the conception of breast cancer treatment. The classic scheme of surgery followed by systemic treatment and radiotherapy is being subverted and it is becoming more and more frequent to propose the primary administration of systemic treatment before surgery, seeking to maximize its effect and favoring not only the performance of more conservative surgeries but also, in selected cases, increasing the rates of disease-free survival and overall survival. Radiotherapy is also evolving toward a change in perspective: considering preoperative primary administration of radiotherapy may be useful in selected groups. Advances in radiobiological knowledge, together with technological improvements that are constantly being incorporated into clinical practice, support the administration of increasingly reliable, precise, and effective radiotherapy, as well as its safe combination with antitumor drugs or immunotherapy in the primary preoperative context. In this paper, we present a narrative review of the usefulness of preoperative radiotherapy for breast cancer patients and the possibilities for its combination with other therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".