Palbociclib (PAL) + letrozole (L) as first-line (1L) therapy (tx) in estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2−) advanced breast cancer (ABC): Efficacy and safety across patient (pt) subgroups.
Bibliographic record
Abstract
1039 Background: Hormone tx (HT) is the primary 1L tx for ER+ ABC. In the PALOMA-2 study (NCT01740427), PAL+L as 1L ABC tx prolonged progression-free survival (PFS; hazard ratio [HR] 0.58; P<.001) (Finn et al, NEJM. 2016). Methods: Postmenopausal pts with ER+/HER2– ABC and no prior systemic treatment in the advanced setting were randomized 2:1 to PAL (125 mg/d oral [3 wk on, 1 wk off]) + L (2.5 mg QD) or placebo (P) + L. Key endpoints were investigator-assessed PFS and safety. Results: 666pts (444, PAL+L; 222, P+L) were enrolled. Pts were similarly distributed between arms for visceral (48%) and nonvisceral (52%) disease and prior HT (56%) and no prior HT (44%); more pts had disease-free interval (DFI) >12 mo (40%) than ≤12 mo (22%). Median PFS (mPFS) was improved in all subgroups by adding PAL to L (Table). Adverse events were consistent across subgroups, as described for the full study population. Conclusions: PAL+L improved mPFS vs P+L with manageable toxicity across all subgroups including those with visceral disease. PAL+L provides a 1L option that should be considered for all pts with ER+/HER2- ABC. Sponsor: Pfizer Clinical trial information: NCT01740427. [Table: see text]
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".