HIV Sero-Prevalence among Infants Attending Immunization Centers in Calabar Metropolis, Cross River State, Southern, Nigeria
Bibliographic record
Abstract
Introduction: Pediatric Human Immunodeficiency Virus (HIV) infection accounts for over 2.3% of all pediatric infections. Many HIV-infected infants are not identified until they develop symptoms and present with illness at health facilities. However, the six weeks immunization visit provides an opportunity for HIV-infected mothers and their exposed infants to be identified before symptoms occur. This study was therefore conducted to determine the HIV status of infants attending immunization clinics in Calabar with a view to enrolling them into treatment. Subjects and Method: This cross sectional descriptive study was conducted in two Local Government Areas of Calabar consisting 22 selected immunization centers. Using the multistage sampling method, 330 infants were screened. Ethical clearance was obtained from the supervising Ministry of Health. Rapid test was conducted, reactive specimens had Deoxyribonucleic Acid Polymerase Chain Reaction (DNA PCR) done using Dried Blood Spots (DBS). Results: A total of 330 infants aged 6 to 14 weeks were recruited, 173 (52.4%) were males while 157(47.6%) were females giving male to female ratio of 1.1:1. Mean age of the infants was 9.20 ± 3.1 weeks. Twenty four (24) tested positive for HIV antibodies, after HIV DNA PCR test, 14(4.2%) infants were infected. Antenatal care registration (ANC) and maternal ANC HIV status were statistically significant P=0.03 and P= 0.02 respectively. Conclusion: HIV exposed and infected infants are still been missed and only diagnosed later in life. Therefore, maternal HIV status determination and early diagnosis at immunization centers is recommended to bridge the Prevention of Mother To child Transmission (PMTCT) gap.
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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 | 0.000 | 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".