Performance of rapid diagnostic test, light microscopy, and polymerase chain reaction in pregnant women with asymptomatic malaria in Nigeria
Bibliographic record
Abstract
Objectives: specific RDT compared with microscopy and polymerase chain reaction (PCR) in diagnosing asymptomatic malaria in pregnant women in southwest Nigeria. Methods: The study included 406 asymptomatic pregnant women seeking antenatal care. Blood samples were collected and tested using RDT (SD Bioline, Standard Diagnostics Inc. Korea) and light microscopy and confirmed using nested PCR. Results: The study revealed that the malaria parasite positivity rate was 8.9% by RDT, 21% by microscopy, and 32% by nested PCR. RDT had a sensitivity of 51.4% and specificity of 69.5%, whereas microscopy had a sensitivity of 65.3% and specificity of 98.2%. The combined testing of microscopy and RDT had a sensitivity and specificity of 100%. The study also showed a high prevalence of mild anemia among participants. Conclusions: Despite the RDT's low sensitivity, its high negative predictive value suggests it could be useful in combination with microscopy in ruling out asymptomatic malaria in pregnancy. Further study will help identify more suitable RDTs for routine malaria diagnosis in Nigeria and strengthen malaria prevention programs in pregnant women.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".