Integrated malaria vector control strategies and their effectiveness in sub-Saharan Africa: a systematic review protocol for interventional studies
Bibliographic record
Abstract
INTRODUCTION: Sub-Saharan Africa has the highest malaria burden in the world. Several vector control strategies are being implemented to reduce mosquito density and protect the most vulnerable populations, such as children under 5 and pregnant women. This systematic review is designed to assess the effectiveness of integrated vector control versus single vector control interventions on malaria incidence and prevalence to guide decisions on controlling malaria vectors in sub-Saharan Africa. METHODS AND ANALYSES: We will systematically retrieve published and grey literature from electronic databases and clinical trial registries. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines will guide us in applying a systematic approach to screening, reviewing and extracting data. An inclusion criterion will be used to independently assess full-text copies of potentially relevant articles by two review authors. Risk of bias will be assessed using the Cochrane Risk of Bias Tool V.2 for randomised controlled trials and the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool for non-randomised intervention studies. A meta-analysis will be conducted based on studies that have reported a high level of evidence (risk ratios or ORs with 95% CIs). If substantial heterogeneity is encountered, subgroup analyses will be explored. ETHICS AND DISSEMINATION: This review does not require ethical approval. The findings will be shared through open-access publications in peer-reviewed journals and presentations to stakeholders and international policymakers for malaria control. PROSPERO REGISTRATION NUMBER: CRD42024559088.
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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 | 0.093 | 0.100 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.016 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.075 | 0.014 |
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".