Patient-Reported Symptoms and Quality of Life With Sacituzumab Govitecan Versus Docetaxel in Metastatic NSCLC: The Phase 3, Randomized EVOKE-01 Trial
Bibliographic record
Abstract
Introduction: In the phase 3 EVOKE-01 trial (NCT05089734), sacituzumab govitecan (SG) exhibited a numerical improvement in overall survival and tolerability compared with docetaxel in patients with metastatic NSCLC previously treated with platinum-based chemotherapy and programmed cell death protein (ligand) 1 inhibitors, although results were not statistically significant. This analysis evaluated health-related quality of life (HRQoL) data from EVOKE-01. Methods: Patients (N = 603) were randomized 1:1 to SG (n = 299) or docetaxel (n = 304) in 21-day cycles. HRQoL was assessed by the NSCLC Symptom Assessment Questionnaire (NSCLC-SAQ), European Organization for Research and Treatment of Cancer Quality of Life questionnaire-Core 30 (QLQ-C30), and EuroQol 5 Dimension 3-level questionnaire. Least square mean changes from baseline at week 25, time to first meaningful deterioration or death, and time to confirmed deterioration were analyzed. Results: SG exhibited significantly and meaningfully better effects than docetaxel on NSCLC-SAQ shortness of breath (SoB), fatigue, total score, and on QLQ-C30 role functioning, fatigue, and dyspnea. Time to first meaningful deterioration or death favored SG over docetaxel for NSCLC-SAQ SoB (hazard ratio [95% confidence interval ]: 0.75 [0.61-0.91]), fatigue (0.70 [0.57-0.86]), and total score (0.80 [0.66-0.97]); QLQ-C30 fatigue (0.80 [0.66-0.96]) and dyspnea (0.74 [0.60-0.90]); and EuroQol visual analog scale (0.79 [0.65-0.96]). The time to confirmed deterioration favored SG over docetaxel for NSCLC-SAQ SoB (0.59 [0.44-0.77]) and fatigue (0.70 [0.52-0.95]), and QLQ-C30 fatigue (0.75 [0.59-0.95]). Conclusions: These exploratory results suggest that SG may benefit HRQoL over docetaxel, supporting SG as an active therapeutic agent for metastatic NSCLC post platinum-based and programmed cell death protein (ligand) 1 inhibitor 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".