Mitigation and management of adverse events associated with amivantamab therapy
Bibliographic record
Abstract
Amivantamab is a fully human bispecific epidermal growth factor receptor (EGFR)-directed and mesenchymal epithelial transition (MET) receptor-directed antibody. Intravenous amivantamab is approved and recommended by treatment guidelines as a first-line treatment (1L) in combination with lazertinib, as a second-line treatment (2L) in combination with chemotherapy in adults with advanced or metastatic non-small cell lung cancer (NSCLC) with EGFR exon 19 deletions or exon 21 L858R substitution mutations, and as 2L monotherapy or 1L in combination with chemotherapy in adults with advanced or metastatic NSCLC with exon 20 insertion-mutations. Compared with previous therapies, novel treatments such as amivantamab may be associated with distinct and unique adverse reactions that potentially require optimized prevention and management techniques. Commonly reported adverse reactions associated with amivantamab treatment regimens include cutaneous reactions associated with EGFR inhibition, such as rash, paronychia, and pruritus; those associated with MET inhibition, such as peripheral edema and hypoalbuminemia; and general effects, such as infusion-related reactions. Recommendations are summarized from published guidelines and the authors' clinical experience for the prevention and management of adverse reactions associated with amivantamab. An understanding of the expected adverse events with amivantamab regimens, along with the range of prophylactic and management options available, may facilitate maintenance of ongoing treatment in patients deriving clinical benefit and improve patient quality of life on therapy.
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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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".