Subcutaneous versus intravenous amivantamab, both in combination with lazertinib, in refractory EGFR-mutated non-small cell lung cancer: Patient satisfaction and resource utilization results from the PALOMA-3 study
Bibliographic record
Abstract
INTRODUCTION: Intravenous anticancer treatments present challenges for patients and healthcare professionals (HCPs), prompting the development of subcutaneous formulations. In the phase 3 PALOMA-3 study, subcutaneous amivantamab demonstrated noninferior pharmacokinetics and response rates versus intravenous amivantamab (both with lazertinib), with substantially faster administration, a 5-fold reduction in infusion-related reactions, reduced venous thromboembolism, and numerically prolonged survival. METHODS: Participants with EGFR-mutated NSCLC and progression on osimertinib and chemotherapy were randomized to subcutaneous (n = 206) or intravenous amivantamab (n = 212), plus lazertinib. Resource utilization and participant-reported treatment satisfaction were evaluated at cycle (C) 1 day (D) 1 and C3D1. RESULTS: Time-in-chair was substantially lower for subcutaneous versus intravenous amivantamab (C1D1: median [range], 23 min or 0.4 h [0-12.0 h] vs 6.5 h [0-24.0 h]; C3D1: 35 min or 0.6 h [0-6.6 h] vs 3.4 h [0.5-9.0 h]), as were HCP time and participant time-in-room. More participants who received subcutaneous versus intravenous amivantamab reported feeling unrestricted (C1D1, 66 % vs 29 %; C3D1, 60 % vs 42 %) or unbothered (C1D1, 69 % vs 30 %; C3D1, 71 % vs 45 %) by administration, and reported gaining time for other activities (C1D1, 36 % vs 7 %; C3D1, 37 % vs 6 %). Few participants who received subcutaneous amivantamab reported moderate-to-very severe injection-site pain (C1D1, 14 %; C3D1, 16 %), swelling (C1D1, 5 %; C3D1, 6 %), or redness (C1D1, 5 %; C3D1, 6 %). Most subcutaneous amivantamab recipients preferred and were more satisfied with its administration versus historical experience with intravenous therapies and would recommend it. CONCLUSIONS: In PALOMA-3, subcutaneous amivantamab, which simplifies and shortens administration, reduces resource utilization, and enhances treatment experience, was a preferred option for patients who received amivantamab-lazertinib.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".