Systematic review and meta-analysis of chemotherapy-induced adverse drug reactions among children with cancer in Africa
Bibliographic record
Abstract
INTRODUCTION: Several systematic reviews have looked at chemotherapy-related toxicities in children with cancer worldwide. In African settings, the rates of ADRs may differ. There is no robust, quantitative estimate of the overall pooled prevalence of chemotherapy-related ADRs among African pediatric oncology patients. Both hematologic and solid tumor patients were included in the analysis. Hence, this study aimed to assess the pooled prevalence of chemotherapy-related adverse drug events among children with all cancer types in Africa. METHODS: We used a comprehensive search of articles using different databases (7 databases) such as Google Scholar, Cochrane Library, Science Direct, Web of Sciences, Scopus, EMBASE, and PubMed to explore relevant studies. Studies published until November 16, 2025 included in the study. The quality of the included studies was assessed using the Newcastle– Ottawa Quality Assessment Scale. Data was extracted with Microsoft Excel and analyzed using STATA version 17 software. Random effect meta-regression analysis at 95% CI was used to assess the pooled prevalence of chemotherapy-related adverse drug events among children with cancer in Africa. Heterogeneity was determined using the I2 statistic, and publication bias was evaluated with a funnel plot and Egger test. RESULT: The pooled prevalence of chemotherapy-induced adverse drug events among pediatric cancer patients in Africa from 9 studies revealed that 62.03% (95%CI: 36.93–87.13) and with (I2 = 99.4, P = 0.000). A Subgroup meta-analysis showed comparatively less heterogeneity in other African countries (I2 = 88.2%, p = 0.000) as compared to studies conducted in Ethiopia (I2 = 99%, P-value = 0.000). Moreover, subgroup meta-analysis showed that there was a difference in the strength of heterogeneity between articles published before 2020(I2 = 99.8%, P-value = 0.000) and after 2020 (I2 = 97.4%, p = 0.000). CONCLUSION: The burden of chemotherapy-related adverse drug reactions was found to be high. Health care professionals involved in cancer treatment should be prepared to deal with chemotherapy-related adverse drug reactions. Routine ADR surveillance should be integrated into pediatric oncology wards in Africa. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".