Treatment Exposure and Discontinuation in the PALbociclib CoLlaborative Adjuvant Study of Palbociclib With Adjuvant Endocrine Therapy for Hormone Receptor–Positive/Human Epidermal Growth Factor Receptor 2–Negative Early Breast Cancer (PALLAS/AFT-05/ABCSG-42/BIG-14-03)
Bibliographic record
Abstract
PURPOSE: The PALLAS study investigated whether the addition of palbociclib, an oral CDK4/6 inhibitor, to adjuvant endocrine therapy (ET) improves invasive disease-free survival (iDFS) in early hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) breast cancer. In this analysis, we evaluated palbociclib exposure and discontinuation in PALLAS. METHODS: Patients with stage II-III HR+, HER2- disease were randomly assigned to 2 years of palbociclib with adjuvant ET versus ET alone. The primary objective was to compare iDFS between arms. Continuous monitoring of toxicity, dose modifications, and early discontinuation was performed. Association of baseline covariates with time to palbociclib reduction and discontinuation was analyzed with multivariable competing risk models. Landmark and inverse probability weighted per-protocol analyses were performed to assess the impact of drug persistence and exposure on iDFS. RESULTS: Of the 5,743 patient analysis population (2,840 initiating palbociclib), 1,199 (42.2%) stopped palbociclib before 2 years, the majority (772, 27.2%) for adverse effects, most commonly neutropenia and fatigue. Discontinuation of ET did not differ between arms. Discontinuations for non-protocol-defined reasons were greater in the first 3 months of palbociclib, and in the first calendar year of accrual, and declined over time. No significant relationship was seen between longer palbociclib duration or ≥ 70% exposure intensity and improved iDFS. In the weighted per-protocol analysis, no improvement in iDFS was observed in patients receiving palbociclib versus not (hazard ratio 0.89; 95% CI, 0.72 to 1.11). CONCLUSION: Despite observed rates of discontinuation in PALLAS, analyses suggest that the lack of significant iDFS difference between arms was not directly related to inadequate palbociclib exposure. However, the discontinuation rate illustrates the challenge of introducing novel adjuvant treatments, and the need for interventions to improve persistence with oral cancer 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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".