Descriptive analysis of durvalumab use in unresectable stage III non-small cell lung cancer in patients treated in Quebec’s University teaching hospitals
Bibliographic record
Abstract
Introduction: Consolidation durvalumab post chemo-radiotherapy (CRT) has been demonstrated to improve survival in locally advanced non-small-cell lung cancer (NSCLC). Real-world data to assess its use and impact on patients, particularly in Quebec, remain limited. Methods: We, therefore, aimed to assess real-world durvalumab use in inoperable stage III NSCLC in Quebec, to describe progression-free survival (PFS) and overall survival (OS) outcomes as reported in the PACIFIC trial, and to evaluate safety and toxicity. Patients were retrospectively reviewed between January 1rst 2019 and December 31st 2020, based on their oncology reference date. Results: One hundred and eight patients treated with CRT were included, among which 82 received durvalumab (75.9%). The mean duration of treatment was 48 weeks [4-52], and 55% of patients completed the full treatment. Median PFS was 40 months in patients treated with CRT + durvalumab vs 6.9 months with CRT alone, with a hazard ratio (HR) of 0.22 (95% confidence interval (CI) 0.13-0.37; p < 0.0001). Limited access to CT scanning during the COVID pandemic, might have led to delayed disease progression detection and thus prolonged PFS. Median OS was > 52.8 months with CRT + durvalumab vs 19 months with CRT alone (HR 0.33, 95% CI 0.18-0.60; p=0.0002). Discussion: These findings support the efficacy and safety profile of durvalumab in real-world settings.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".