Incidence Rates of Cutaneous Immune-Related Adverse Events in Patients with Lung Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Cutaneous immune-related adverse events (cirAEs) represent a prevalent manifestation of adverse reactions linked to immune checkpoint inhibitors (ICIs) therapy, substantially affecting patients' quality of life. This systematic review and meta-analysis aimed to quantify the pooled incidence of cirAEs in this population and strengthen clinical awareness for early recognition and management. METHODS: A comprehensive search of PubMed, Embase, CINAHL, Cochrane Library, CBM, CNKI, and Wanfang databases was conducted from inception to December 2022. Literature that reported the incidence of cirAEs in patients with lung cancer receiving ICIs therapy was included. A meta-analysis was conducted using R software, version 4.4.1 to estimate the pooled incidence of cirAEs, and a random-effects model was used for data synthesis. Begg's rank correlation and funnel plots were used to assess publication bias. RESULTS: A total of 99 articles involving 23,814 patients with lung cancer receiving ICIs therapy were included, with publication dates ranging from 2012 to 2022. The meta-analysis results reveal that the incidence of cirAEs in patients with lung cancer was 20.26% (95% confidence interval [CI (17.12-23.81)]. Significant differences were observed between all subgroups, including continent, study type, combination therapy, dual ICIs therapy, and diagnostic criteria for cirAEs for Grade 1-2 and Grade 3-4 incidences. CONCLUSIONS: The incidence of cirAEs in patients with lung cancer is relatively high, particularly undergoing combined or dual ICIs therapy. To comprehensively characterize cirAEs in patients with lung cancer, large-scale multicenter studies integrating real-world pharmacovigilance data are warranted to establish precise incidence estimates and identify clinically significant risk factors. IMPLICATIONS FOR CLINICAL PRACTICE: This review's insights aroused clinical staff's attention and concern about cirAEs, potentially enhancing the quality of life of patients with cancer.
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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".