Health Utility Analysis of Tepotinib in Patients With Non-Small Cell Lung Cancer Harboring MET Exon 14 Skipping
Bibliographic record
Abstract
OBJECTIVES: The VISION trial showed durable activity of tepotinib in MET exon 14 (METex14) skipping non-small cell lung cancer. We analyzed health state utilities using patient-reported outcomes from VISION. METHODS: 5-level version of EQ-5D (EQ-5D-5L) and European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 responses were collected at baseline, every 6 to 12 weeks during treatment, and at the end of treatment and safety follow-up. EQ-5D-5L and European Organisation for Research and Treatment of Cancer Quality of Life Utility Measure-Core 10 Dimensions (QLU-C10D) utilities were derived using United States, Canada, United Kingdom, and Taiwan value sets, where available. Utilities were analyzed with linear mixed models including covariates for progression or time-to-death (TTD). RESULTS: Utilities were derived for 273/291 patients (EQ-5D-5L, 1545 observations; QLU-C10D, 1546 observations). Mean (± SD) US EQ-5D-5L utilities increased after tepotinib initiation, from 0.687 ± 0.287 at baseline to 0.754 ± 0.250 before independently assessed progression, and decreased post progression (0.704 ± 0.288). US QLU-C10D utilities showed similar trends (0.705 ± 0.215, 0.753 ± 0.195, and 0.708 ± 0.209, respectively). Progression-based models demonstrated a statistically significant impact of progression on utilities and predicted higher utilities pre versus post progression. TTD-based models showed statistically significant associations of TTD with utilities and predicted declining utilities as TTD decreased. Prior treatment (yes/no) did not significantly predict utilities in progression- or TTD-based models. Utilities for Canada, United Kingdom, and Taiwan showed comparable trends. CONCLUSIONS: In this first analysis of health state utilities in patients with METex14 skipping non-small cell lung cancer, who received tepotinib, utilities were significantly associated with progression and TTD, but not prior treatment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".