Treatment Patterns and Resource Use After Osimertinib Discontinuation in Patients with EGFR + Metastatic NSCLC
Bibliographic record
Abstract
INTRODUCTION: Current treatment guidelines for patients with epidermal growth factor receptor (EGFR)-mutated metastatic non-small cell lung cancer (mNSCLC) recommend EGFR tyrosine kinase inhibitors (TKIs) as the standard of care for first-line treatment, with third-generation osimertinib the preferred choice. However, most patients develop resistance to targeted therapy, and subsequent systemic chemotherapy is recommended. The aim of this study was to characterize the subsequent line of therapy (LOT) following osimertinib in patients with EGFR-mNSCLC. METHODS: Medical and pharmacy claims of adults who initiated a subsequent LOT (index) after initial osimertinib discontinuation between November 2015 and September 2019 were analyzed retrospectively. RESULTS: A total of 135 patients met the inclusion criteria. After metastatic diagnosis, 22.2% and 49.6% of patients were treated with osimertinib in the first and second line, respectively. After osimertinib discontinuation, most patients were treated with a platinum-based chemotherapy regimen (57%), of which 40.3% included immuno-oncology therapy. Reuse or continuation of EGFR TKIs was also common (24%). Overall, the median time to treatment discontinuation for the index LOT was 2.4 months. Proportions of patients with ≥ 1 inpatient or emergency department visit were 31.9% and 35.6%, respectively. CONCLUSIONS: The duration of the LOT following osimertinib was short and associated with tolerability issues underscoring a high unmet need for new therapies to address EGFR TKI resistance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".