The Critical Need for Pooled Data on Coronavirus Disease 2019 in African Children: An AFREhealth Call for Action Through Multicountry Research Collaboration
Bibliographic record
Abstract
Globally, there are prevailing knowledge gaps in the epidemiology, clinical manifestations, and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection among children and adolescents; and these gaps are especially wide in African countries. The availability of robust age-disaggregated data is a critical first step in improving knowledge on disease burden and manifestations of coronavirus disease 2019 (COVID-19) among children. Furthermore, it is essential to improve understanding of SARS-CoV-2 interactions with comorbidities and coinfections such as human immunodeficiency virus (HIV), tuberculosis, malaria, sickle cell disease, and malnutrition, which are highly prevalent among children in sub-Saharan Africa. The African Forum for Research and Education in Health (AFREhealth) COVID-19 Research Collaboration on Children and Adolescents is conducting studies across Western, Central, Eastern, and Southern Africa to address existing knowledge gaps. This consortium is expected to generate key evidence to inform clinical practice and public health policy-making for COVID-19 while concurrently addressing other major diseases affecting children in African countries.
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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 | 0.485 | 0.545 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".