Malaria remains the most important cause of childhood mortality and morbidity and accounted for 63.4% of all reported diseases in Nigeria. The present study is aimed at determining in the prevalence of malaria amongst children 0 - 4 years in Olugbo, Odeda
Bibliographic record
Abstract
Malaria remains the most important cause of childhood mortality and morbidity and accounted for 63.4% of all reported diseases in Nigeria. The present study is aimed at determining in the prevalence of malaria amongst children 0-4 years in Olugbo, Odeda Local Government, Ogun State, Nigeria. Olugbo, the study area is a rural community that consists of fifteen (15) adjoining rural villages, Obosokoto, Idi-obi, Eleta, Aralamo, Akide, Yakoyo, Ogbonsode, Olugbo, Alagbayun, Ilafi, Iyanbu, Koku, Gbagura, Aariku, Idi-omo, villages. A total of two hundred children 0- 48 months were recruited for the purpose of this study. Two millilitres of blood samples were collected by vernipunture. The blood samples were then preserved with an ice pack in a cold box before examination and was analysed using the Quantitative Buffy Coat analyser. The overall prevalence of malaria infection in the present study is 63.0%. The prevalence of infection across the age group is 37.74%, 77.63%, 76.74% and 50.0% for children aged 0-12,13-24, 25-36 and 37-48 months respectively. A significant difference (p< 0.05) exists between malaria infections across the age group of the children enrolled into the study. Free malaria diagnosis and treatment is recommended for children under five years of age.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".