Real‐world health utility scores and toxicities to tyrosine kinase inhibitors in epidermal growth factor receptor mutated advanced non‐small cell lung cancer
Bibliographic record
Abstract
BACKGROUND: As the treatment landscape in patients with non-small cell lung cancer (NSCLC) harboring mutations in the epidermal growth factor receptor (EGFRm) continues to evolve, real-world health utility scores (HUS) become increasingly important for economic analyses. METHODS: In an observational cohort study, questionnaires were completed in EGFRm NSCLC outpatients, to include demographics, EQ-5D-based HUS and patient-reported toxicity and symptoms. Clinical and radiologic characteristics together with outcomes were extracted from chart review. The impact of health states, treatment type, toxicities, and clinical variables on HUS were evaluated. RESULTS: Between 2014 and 2018, a total of 260 patients completed 994 encounters. Across treatment groups, patients with disease progression had lower HUS compared to controlled disease (0.771 vs 0.803; P = .01). Patients predominantly received gefitinib as the first-line EGFR tyrosine kinase inhibitor (TKI) (n = 157, mean-HUS = 0.798), whereas osimertinib (n = 62, mean-HUS = 0.806) and chemotherapy (n = 38, mean-HUS = 0.721) were more likely used in subsequent treatment lines. In longitudinal analysis, TKIs retained high HUS (>0.78) compared to chemotherapy (HUS < 0.74). There were no differences between the frequency or severity of toxicity scores in patients receiving gefitinib compared to osimertinib; however, TKI therapy resulted in fewer toxicities than chemotherapy (P < .05), with the exception of worse diarrhea and skin rash (P < .001). Severity in toxicities inversely correlated with HUS (P < .001). Clinico-demographic factors significantly affecting HUS included age, Eastern Cooperative Oncology Group Performance Score (ECOG PS), disease state, treatment group, and metastatic burden. CONCLUSIONS: In a real-world EGFRm population, patients treated with gefitinib or osimertinib had similar HUS and toxicities, scores which were superior to chemotherapy. Health utility scores inversely correlated with patient-reported toxicity scores. In the era of targeted therapies, future economic analyses should incorporate real-world HUS.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".