Safety and efficacy analysis of pembrolizumab dosing patterns in patients with advanced melanoma and non-small cell lung cancer
Bibliographic record
Abstract
AIM: To evaluate the impact of discrepancy between prescribed and recommended fixed 200 mg dose (P-F discrepancy) on immune-related adverse events (irAEs) and treatment efficacy in patients with advanced melanoma and NSCLC. METHODS: This retrospective study included 177 patients with advanced melanoma or non-small cell lung cancer (NSCLC) who received at least one cycle of single-agent pembrolizumab. We defined P-F discrepancy as the differences between prescribed pembrolizumab dose and 200 mg recommended dose, expressed in percentages. Our primary outcome was immune-related adverse events (irAEs), and our secondary outcomes included overall survival (OS) and progression free survival (PFS). RESULTS: The median P-F discrepancy was -21.5%, with the 25th and 75th percentile at -32% and -5.0% respectively. ROC curve analyses did not show any optimal cutoffs to prognosticate irAEs (AUC = 0.558 for all patients) or cancer mortality (AUC = 0.583 for melanoma; AUC = 0.539 for NSCLC) in either cancer type. Separate multivariable Cox analyses suggested no statistically significant association between P-F discrepancy and overall survival in patients with melanoma (HR 1.012, 95%CI 0.987-1.038, P = 0.362) or NSCLC (HR 0.998, 95%CI 0.978-1.019, P = 0.876). CONCLUSION: There was no optimal pembrolizumab cut-off point to predict irAEs or treatment efficacy. We supported the use of weight-based pembrolizumab dosing, given the potential cost-saving and no differences in terms of irAEs or treatment efficacy in patients with advanced melanoma or NSCLC. Future studies on province- or national-level would be important to validate our findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".