Prevalence of malaria among COVID-19 suspected cases in Federal Capital Territory, Nigeria
Bibliographic record
Abstract
Abstract Malaria and COVID-19 share some symptoms. Therefore, diagnosing these diseases clinically might be misleading, especially during an epidemic response. We determined the prevalence of malaria among COVID-19 suspected cases in Federal Capital Territory, Nigeria. This study was conducted in five selected health facilities in Abuja, with participation of 254 febrile patients attending COVID-19 screening centres in those facilities. Each subject was interviewed using a structured interviewer-administered questionnaire. Samples were collected for malaria and COVID-19 testing. Descriptive statistical analysis was done and included means, standard deviations, and proportions. Results were presented in form of tables and figures. There were 254 participants with median age of 34 years (range: 18–80). The age group, 30–49 years had the highest representation among the participants (55.1%). Majority were females (53.1%), with tertiary education (66.9%), were businessmen/women (37.4%) and civil servants (24.8%). More than half of participants were married (54%). Only 2% tested positive for malaria, and 1.2% for COVID-19. No participant presented with co-infection. The prevalence of COVID-19 among patients presenting themselves for COVID-19 testing was low while the prevalence of malaria was also low. There is still a need to test all suspected cases of COVID-19 that present in a testing centre for malaria.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".