Clinician’s guide: expert insights on the use of CDK4/6 inhibitors in patients with early breast cancer
Bibliographic record
Abstract
The introduction of the cyclin-dependent kinases 4 and 6 (CDK4/6) inhibitors abemaciclib and ribociclib to the adjuvant setting marks a significant advancement in the treatment of hormone-receptor-positive, human epidermal growth factor receptor 2-negative early breast cancer (HR+, HER2- EBC). Despite significant strides in early detection and treatment, many patients continue to face the risk of disease recurrence, highlighting the need for more effective adjuvant therapies. These CDK4/6 inhibitors, combined with adjuvant endocrine therapy, have shown promising efficacy in reducing recurrence rates while maintaining a manageable safety profile, as evidenced by the monarchE and NATALEE trials. This paper explores the integration of adjuvant CDK4/6 inhibitors into clinical practice, focusing on disease-free survival and safety outcomes. Key considerations in selecting between abemaciclib and ribociclib are discussed, including patient risk profiles, efficacy and safety profiles, treatment duration, and individual patient preferences. In addition, we discuss managing adverse events to prevent premature discontinuation, with strategies that include dose holds, dose reductions, proactive symptom management, and patient education. The paper also highlights strategies to enhance patient medication adherence and the involvement of multidisciplinary care teams to support treatment delivery. As research continues to evolve, additional follow-ups of the monarchE and NATALEE trials and future trials will further refine patient selection and treatment sequencing, ultimately improving outcomes and enhancing the quality of life for patients with HR+, HER2- EBC.
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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.095 | 0.076 |
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".