Real-World Clinical Outcomes of Ribociclib in Combination with a Non-Steroidal Aromatase Inhibitor and a Luteinizing Hormone-Releasing Hormone Agonist in Premenopausal HR+/HER2− Advanced Breast Cancer Patients: An Italian Managed Access Program
Bibliographic record
Abstract
Ribociclib plus an aromatase inhibitor and ovarian function suppression is the preferred first-line option for pre-/perimenopausal women with hormone receptor-positive/human epidermal growth factor receptor-2-negative advanced or metastatic breast cancer. We opened an italian managed access program (MAP) that permitted access to ribociclib to selected patients and allowed to collect informative results on the clinical impact of the therapy. The MAP (April 2018-May 2020) included 64 premenopausal patients, with characteristics similar to those of the MONALEESA-7 trial. Of 57 patients with a known response, 48 (84.2%) achieved a clinical benefit (i.e., complete response, N = 7 (12.3%); partial response, N = 17 (29.8%); stable disease, N = 24 (42.1%)), while 9 (15.8%) experienced tumor progression. Some patients (N = 15-23.4%) needed ribociclib dose reduction because of adverse events. Thereafter, the treatment was well tolerated, and no new safety signals emerged. Our study is the first reported Italian real-world evidence of ribociclib effectiveness in premenopausal HR+/HER2- advanced breast cancer patients. Response and clinical benefit rates were particularly encouraging compared with those of the ribociclib group of MONALEESA-7. Our work confirms that ribociclib in combination with endocrine therapy is highly effective in the treatment of premenopausal HR+/HER2- advanced breast cancer patients with an expected safety profile.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".