The Prevalence of 5-Fluorouracil and Capecitabine Cardiotoxicity: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The incidence of cardiotoxicity events in patients who use 5-fluorouracil (5-FU) and capecitabine monotherapy remains unclear since previous studies reported the prevalence in patients who used combination regimens. We aimed to systematically review and meta-analyze the incidence of cardiotoxicity in fluorouracil and capecitabine monotherapy users. Methods: The study protocol was registered with PROSPERO (CRD42023441627). Systematic searches were conducted in five databases (CINAHL, OpenGrey, PubMed, ScienceDirect, and Scopus). The Cochrane Risk-of-Bias tool and the Risk Of Bias In Non-randomized Studies were used to evaluate the risk of bias. Pooled prevalence and 95% confidence interval (CI) were calculated using the DerSimonian-Laird random effect models. The funnel plot was used to assess the publication bias. Results: Eighty studies were included. There were 24 randomized controlled trials (RCTs) with low to high risk of bias and 56 non-RCTs with critical risk of bias. The pooled prevalence of cardiotoxicity from 5-FU was 3.5% (95% CI: 2.7 - 4.2; P < 0.001; I2 = 73.86%). The pooled prevalence of cardiotoxicity in capecitabine users was 2.8% (95% CI: 1.6 - 4.0; P < 0.001; I2 = 72.62%). Conclusions: The prevalence of cardiotoxicity from 5-FU and capecitabine was classified as common. Cardiotoxicity may have not been associated with the cumulative dose of 5-FU or capecitabine.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.041 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".