Descriptive Analysis of First-Line Non-Small Cell Lung Cancer Treatment with Pembrolizumab in Tumors Expressing PD-L1 ≥ 50% in Patients Treated in Quebec’s University Teaching Hospitals (DALP-First Study)
Bibliographic record
Abstract
Since July 2017, pembrolizumab has been approved as a first-line treatment of metastatic non-small cell lung cancer (NSCLC) in patients with a PD-L1 score ≥ 50% in Quebec. Study objectives were to describe and assess the real-world use of pembrolizumab; report progression-free survival (PFS), overall survival (OS), and immune-related adverse events (IRAEs); and compare outcomes between a fixed dose (FD) and a weight-based capped dose (WCD). Medical records of patients treated in one of Quebec's four adult university teaching hospitals who received pembrolizumab between 1 November 2017 and 31 October 2019 were reviewed and followed until 29 February 2020. Two hundred and seventy-nine patients were included. The median real-world PFS and OS were 9.4 (95% CI, 6.6 to 11.2) and 17.3 months (95% CI, 12.9 to not reached), respectively. IRAEs causing delays or treatment interruptions were seen in 34.4% of patients. Initiating treatment with a FD (49 patients) or using a WCD (230 patients) does not appear to affect PFS, OS, or the occurrence of IRAEs. The use of a WCD strategy allowed approximately CAD 5.8 million in savings during the course of our study. These findings support the effectiveness and safety of pembrolizumab in a real-world setting. The use of a WCD does not appear to have a negative impact on patient outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".