Real-World Treatment Patterns and Outcomes Among Patients with Early Non-Small Cell Lung Cancer
Bibliographic record
Abstract
Worldwide, about two million people are diagnosed with lung cancer each year, 85% of whom have non-small cell lung cancer (NSCLC). Recent progress in treating advanced/metastatic NSCLC with targeted therapies has shifted attention to early NSCLC (Stages I-IIIA) and perioperative (neoadjuvant and adjuvant) systemic therapies. However, our comprehension of how targeted therapeutics are incorporated into care and their impact on patient outcomes is just starting to unfold. METHODS: This retrospective observational study used a US nationwide electronic health record-derived deidentified database spanning January 2019-March 2024 and aimed to describe (1) eNSCLC patient demographic and clinical characteristics, (2) real-world neoadjuvant and adjuvant use, and (3) patient outcomes. RESULTS: = 1458) did not. Many definitive treatment patients received some perioperative systemic therapy (surgery: 52.6%, radiation: 52.2%, chemoradiation: 75.5%). Neoadjuvant use was limited in all groups (surgery: 8.2%, radiation: 6.1%, chemoradiation: 11.6%). Among the 54.6% receiving adjuvant, immune checkpoint inhibitors were the most common choice for definitive radiation (39.1%) and chemoradiation (73.7%) patients, while surgical patients predominantly received platinum-doublet therapy (37.0%). Surgical patient outcomes were similar across all groups, while definitive chemoradiation or radiation patients without systemic therapy had lower survival rates. CONCLUSIONS: In this study, we found that although the majority of patients underwent some form of definitive treatment, adjuvant use was limited, and neoadjuvant use was rarely included in care. A crucial initial step in improving patient outcomes is to understand and address the underutilization of neoadjuvant/adjuvant systemic therapy for eNSCLC patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".