Incidence of Capillary Leak Syndrome as an Adverse Effect of Drugs in Cancer Patients: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Capillary leak syndrome (CLS) is a rare disease with profound vascular leakage, which can be associated with a high mortality. There have been several reports on CLS as an adverse effect of anti-cancer agents and therapy, but the incidence of CLS according to the kinds of anti-cancer drugs has not been systemically evaluated. Thus, the aim of our study was to comprehensively meta-analyze the incidence of CLS by different types of cancer treatment or after bone marrow transplantation (BMT). We searched the literatures (inception to July 2018) and among 4612 articles, 62 clinical trials (studies) were eligible. We extracted the number of patients with CLS, total cancer patients, name of therapeutic agent and dose, and type of cancer. We performed a meta-analysis to estimate the summary effects with 95% confidence interval and between-study heterogeneity. The reported incidence of CLS was categorized by causative drugs and BMT. The largest number of studies reported on CLS incidence during interleukin-2 (IL-2) treatment (n = 18), which yielded a pooled incidence of 34.7% by overall estimation and 43.9% by meta-analysis. The second largest number of studies reported on anti-cluster of differentiation (anti-CD) agents (n = 13) (incidence of 33.9% by overall estimation and 35.6% by meta-analysis) or undergoing BMT (n = 7 (21.1% by overall estimation and 21.7% by meta-analysis). Also, anti-cancer agents, including IL-2 + imatinib mesylate (three studies) and anti-CD22 monoclinal antibodies (mAb) (four studies), showed a dose-dependent increase in the incidence of CLS. Our study is the first to provide an informative overview on the incidence rate of reported CLS patients as an adverse event of anti-cancer treatment. This meta-analysis can lead to a better understanding of CLS and assist physicians in identifying the presence of CLS early in the disease course to improve the outcome and optimize management.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| 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".