Abstract PS10-14: Efficacy and safety of palbociclib (PAL) in patients (pts) with estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) with preexisting conditions: A post hoc analysis of PALOMA-2
Bibliographic record
Abstract
Abstract Background: In the PALOMA-2 trial, PAL + letrozole (LET) significantly prolonged progression-free survival (PFS) vs placebo (PBO) + LET in pts with ER+/HER2– ABC. This post hoc analysis assessed efficacy and safety of PAL + LET in pts from PALOMA-2 with baseline preexisting conditions grouped by Medical Dictionary for Regulatory Activities (MedDRA) System Organ Class (SOC). Methods: Postmenopausal pts with ER+/HER2– ABC received PAL (125 mg/d, 3/1 schedule) + LET (2.5 mg/d, continuous) or PBO + LET. Pts were grouped by the following MedDRA SOC preexisting conditions: Gastrointestinal, Musculoskeletal, Metabolism, and Vascular/Cardiac. Baseline characteristics, PFS, and safety were assessed. Results: At baseline, 41.4% of pts had preexisting gastrointestinal disorders, 58.6% musculoskeletal disorders, 38.9% metabolism disorders, and 57.4% vascular/cardiac disorders. Baseline characteristics were similar between treatment arms within each subgroup and also between subgroups. Within each subgroup, ≥40% of pts also had ≥1 of the other coexisting conditions. Median PFS (mPFS) was significantly longer with PAL + LET vs PBO + LET regardless of preexisting condition (Table). In general, adverse events (AEs) were more frequent with PAL + LET in all subgroups; neutropenia was most common. Within each treatment arm, AEs and dose modifications due to AEs were similar regardless of preexisting condition. Conclusion: PAL + LET showed prolonged PFS and a consistent safety profile regardless of baseline preexisting condition in pts with ER+/HER2– ABC. Clinical trial identification: Pfizer Inc (NCT01740427) TablePreexisting ConditionPAL + LETPBO + LETPAL + LET vs PBO + LETnmPFS (95% CI)nmPFS (95% CI)HR (95% CI)P ValueGastrointestinal17627.6 (17.5–33.1)10013.6 (11.0–18.5)0.57 (0.42–0.78)<0.001Musculoskeletal25227.6 (21.4–33.1)13816.3 (11.2–19.1)0.53 (0.41–0.69)<0.001Metabolism18627.6 (19.3–30.6)7313.8 (8.3–27.4)0.62 (0.44–0.87)0.003Vascular/Cardiac25430.4 (25.1–36.2)12814.5 (11.0–18.5)0.51 (0.39–0.66)<0.001 Citation Format: Karen Gelmon, Janice M Walshe, Reshma Mahtani, Anil A Joy, Meghan Karuturi, Patrick Neven, Dongrui Ray Lu, Sindy Kim, Patrick Schnell, Eustratios Bananis, Lee Schwartzberg. Efficacy and safety of palbociclib (PAL) in patients (pts) with estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer (ABC) with preexisting conditions: A post hoc analysis of PALOMA-2 [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS10-14.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".