Burden, Risk Factors, and Clinical Outcomes of Pediatric Malaria in Nigeria: A Systematic Review and Meta-Analysis Protocol
Bibliographic record
Abstract
Abstract Background Nigeria ranks number one globally in malaria burden, with the exact burden, especially for hospitalization, unknown. This systematic review and meta-analysis intend to fill this gap by pooling the analysis of pediatric malaria and associated factors in Nigeria. Methods and Analysis A comprehensive search strategy using MeSH terms, text words, and entry terms will be applied to six databases: PubMed, Embase, EBSCO Hosts, Google Scholar, Web of Science, and Scopus. Eligible studies will be observational and published in English from inception till June 30, 2025. The primary outcome is the pooled prevalence of pediatric malaria [overall, severe malaria vs. uncomplicated]. Secondary outcomes include factors influencing clinical presentation, severity, and outcomes, as well as the effects of moderators such as age, sex, socioeconomic status, and geographic location. Data extraction will capture study characteristics, participant demographics, and outcome measures. Methodological, clinical, and statistical heterogeneity will be assessed. Risk of bias will be evaluated using the Newcastle-Ottawa Scale. Publication bias will be examined using funnel plots, Egger’s regression. Pooled estimates will be reported with 95% confidence intervals. This will summarize the data on pediatric malaria in Nigeria. Using the random-effects models, the pooled prevalence along with the 95% CI and the I 2 for the test of heterogeneity will be reported. We will report the meta-regression analysis of factors influencing pediatric malaria in Nigeria. Conclusions This study will provide robust data on pediatric malaria and associated factors in Nigeria. The findings from this study will inform the country’s policy and public health approach as the nation strives to eliminate malaria in line with the WHO’s goals. PROSPERO Registration Number CRD420251163322
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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 | 0.048 | 0.072 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.005 |
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".