Real-life use of nivolumab and pembrolizumab in four adult university teaching hospitals in Québec, Canada.
Bibliographic record
Abstract
e14125 Background: Nivolumab and pembrolizumab, two anti-PD1 agents, were approved and funded in Québec since 2016 for non small cell lung cancer (NSCLC), renal cell carcinoma (RCC) and melanoma. The objectives were to describe and assess the “real-life” use, efficacy and security of nivolumab and pembrolizumab in NSCLC, RCC and melanoma in the general population. Methods: Medical records of every patient who received nivolumab or pembrolizumab between January 1st 2011 and October 31st 2017 were reviewed retrospectively. Data analysis cut-off was Dec 31st 2017. Results: In total, 532 patients received at least one dose of anti-PD1 during the study period. Median number of doses received varied for each indication (medians varied from 4 to 9.5). Adverse events were pooled together by drug. 47.7 % of patients receiving pembrolizumab suffered from any grade immune-related adverse event (IRAE), most of them of grade 1 or 2. 12.2 % of patients reported grade 3-4 IRAE. Most of the patients reported only one type of IRAE. For nivolumab, 44.6% of patients presented with any IRAE, including 8.3% of grade 3-4. Dermatologic IRAE were more frequent in the melanoma patients whereas gastrointestinal and pulmonary IRAE were more frequent in NSCLC patients. Treatment discontinuation due to adverse events varied from 6 to18% depending on indication. Conclusions: Nivolumab and pembrolizumab seemed less effective and caused more IRAE in “real-life” population than in the pivotal clinical trials. Caution and regular follow-up are warranted when using these drugs in general population. Longer follow-up is needed.[Table: see text]
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".