The incidence of pseudoprogressive disease associated with programmed cell death 1/programmed cell death ligand 1 inhibitors
Bibliographic record
Abstract
BACKGROUND: The method to evaluate the efficacy of programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors has become a big concern for researchers with its widely application. Pseudoprogressive disease (PPD) makes this process more difficult, which means that the tumor progressed at the initial evaluation, but re-evaluation after continued treatment suggested that the treatment was effective. However, PPD has not attracted enough attention of clinical doctors. This article is to systematically evaluate the incidence of PPD associated with PD-1/PD-L1 inhibitors with meta-analysis, to provide guidance for the recognition and management of PPD. METHODS: The databases of PubMed, EMBase, Cochrane Library were retrieved from the earliest collection date of the databases until Dec 5, 2019. The search terms of "pseudoprogressive disease, anti-PD-1, anti-PD-L1, PD-1/PD-L1 inhibitor, etc" were used for logistic combination search. Published studies on PPD caused by PD-1/PD-L1 inhibitors were included. Meta-analysis was performed with Stata 15.1. Subgroup analysis was performed according to the study population, tumor type, and evaluation criteria for efficacy. RESULTS: Seven researches, including 1458 patients were taken into the study. Meta-analysis showed that the overall incidence of PPD was 3.70% (95% confidence interval [CI]: 2.70%, 4.90%). Subgroup analysis showed that the incidence of PPD was 3.30% (95% CI: 1.90%, 5.90%) in non-small cell lung cancer patients and 5.10% (95% CI: 2.30%, 11.6%) in melanoma patients. There was no statistically significant difference between East and West populations and among various efficacy evaluation criteria. CONCLUSION: The incidence of PPD related to PD-1/PD-L1 inhibitors is not high, but the evaluation criteria has not yet been unified. Close monitoring, careful identification and proper application should be carried out in the clinic, and full management of the treatment with PD-1/PD-L1 inhibitors should be well done.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.026 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".