New Therapeutic Scenarios in the Context of Adjuvant Treatment for HR+/HER2−Breast Cancer: The Possible Role of Ribociclib in Treatment Algorithms for Stage II and III
Bibliographic record
Abstract
Early breast cancer (EBC) treatment has evolved from radical surgery to a multidisciplinary approach, integrating radiotherapy, chemotherapy, targeted therapy, and hormone therapy with surgery to ensure the best possible outcome. Despite these advancements, hormone receptor-positive (HR+)/Human Epidermal Growth Factor Receptor 2-Negative (HER2-) EBC still faces high recurrence rates after endocrine therapy. A panel of oncologists from Central-Southern Italy discussed the profile of ribociclib as an adjuvant therapy, based on the results of the NATALEE study, focusing on efficacy, safety, patient profiles, and regional challenges in treatment access. The experts identified ribociclib as suitable adjuvant treatment for stage II and III HR+/HER2- EBC patients, including those without lymph node involvement but with biologically aggressive disease. In their view, ribociclib could be an interesting option for patients not eligible for chemotherapy due to contraindications. Key challenges in translating the evidence on ribociclib in EBC into clinical practice include treatment duration, patient follow-up, and adverse events management. Strategies to address these challenges range from telemedicine and support from local clinics to tailored communication to improve adherence. Ribociclib is expected to significantly impact adjuvant treatment for HR+/HER2- EBC by addressing broader patient needs and potentially improving long-term outcomes through enhanced adherence and personalized management strategies.
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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".