Durvalumab After Concurrent Chemoradiotherapy in Elderly Patients With Unresectable Stage III Non–Small–Cell Lung Cancer (PACIFIC)
Bibliographic record
Abstract
BACKGROUND: The PACIFIC trial demonstrated that consolidation durvalumab significantly improved PFS and OS (the primary endpoints) vs. placebo in patients with unresectable, stage III NSCLC whose disease had not progressed after platinum-based, concurrent chemoradiotherapy (CRT). We report exploratory analyses of outcomes from PACIFIC by age. PATIENTS AND METHODS: Patients were randomized 2:1 (1-42 days post-CRT) to receive 12-months' durvalumab (10 mg/kg intravenously every-2-weeks) or placebo. We analyzed PFS and OS (unstratified Cox-proportional-hazards models), safety and patient-reported outcomes (PROs: symptoms, functioning, and global-health-status/quality-of-life) in subgroups defined by a post-hoc 70-year age threshold. Data cut-off for PFS was February 13, 2017 and for OS, safety and PROs was March 22, 2018. RESULTS: Overall, 158 of 713 (22.2%) and 555 of 713 (77.8%) randomized patients were aged ≥70 and <70 years, respectively. Durvalumab improved PFS and OS among patients aged ≥70 (PFS: hazard ratio [HR], 0.62 [95% CI, 0.41-0.95]; OS: HR, 0.78 [95% CI, 0.50-1.22]) and <70 (PFS: HR, 0.53 [95% CI, 0.42-0.67]; OS: HR, 0.66 [95% CI, 0.51-0.87]), although the estimated HR-95% CI for OS crossed one among patients aged ≥70. Durvalumab exhibited a manageable safety profile and did not detrimentally affect PROs vs. placebo, regardless of age; grade 3/4 (41.6% vs. 25.5%) and serious adverse events (42.6% vs. 25.5%) were more common with durvalumab vs. placebo among patients aged ≥70. CONCLUSION: Durvalumab was associated with treatment benefit, manageable safety, and no detrimental impact on PROs, irrespective of age, suggesting that elderly patients with unresectable, stage III NSCLC benefit from treatment with consolidation durvalumab after CRT. However, small subgroup sizes and imbalances in baseline factors prevent robust conclusions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".