A Systematic Review Protocol to Establish Plasmodium Falciparum Genetic Diversity, Multiplicity of Infection, Genotyping Approaches, and Methods of Reporting It in Africa.
Bibliographic record
Abstract
Abstract BackgroundP. falciparum is the most important Plasmodium species that causes high malaria morbidity and mortality. The distribution of diverse and multiple P. falciparum infections is a major setback to the control and eventual elimination of malaria globally. Little efforts have been made to systematically establish P. falciparum genetic diversity and multiplicity of infection (MOI) in African settings. Additionally, the choice of an effective P. falciparum genotyping approach for a specific endemic setting remains a challenge. The review aims to establish P. falciparum genetic diversity MOI, genotyping approaches, and methods of reporting it in Africa. This will aid the evaluation of the impact of malaria control interventions and development of new control strategies.MethodsThe review will consider Randomized Clinical Trials (RCTs), Quasi-experiments, Cross-sectional studies, Cohort and Case-control studies about P. falciparum genetic diversity and MOI. A literature search will be conducted in PubMed, Google scholar and MEDLINE databases for articles published from January 2010 to December 2020. Articles will be screened using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Risk of bias in RCTs will be assessed using Cochrane risk of bias assessment tool while New Castle Ottawa tools will be used to assess the risk of bias in observational studies. Publication bias will be assessed using a funnel plot as well as Begg and Mazumdar's rank correlation test or Egger's test. The Higgins I2 statistic and Galbraith plots will be used to assess heterogeneity. A meta-regression analysis will be performed to explain the low heterogeneity. DiscussionThe findings from the study will enhance our understanding of the distribution and dynamics of P. falciparum genetic diversity and MOI. This will provide insights to the changing landscape of malaria transmission and evaluation of malaria control interventions. Findings will also serve as baseline data for future studies on parasite population structure and antimalarial drug resistance surveillance across African countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.121 | 0.134 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.025 | 0.018 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.088 | 0.013 |
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".