Recruitment and engagement of a cohort of women living with HIV in Nigeria: Baseline characteristics from the Nigeria Implementation Science Alliance
Bibliographic record
Abstract
Nigeria has a high burden of mother to child transmission (MTCT) of HIV. There is paucity of large-scale prospective cohort studies to provide insight into the reasons for the abysmal MTCT indices. This paper describes the baseline characteristics of women living with HIV who signed consent to participate in future clinical or implementation trials. The Nigeria Implementation Science Alliance (NISA) developed an open multicentre prospective cohort of women of reproductive age living with HIV, drawn from 12 facilities across the six geo-political regions of Nigeria. Research Electronic Data Capture system was used for the informed consent process. Socio-demographic and clinical information of participants were accessed through the clinics' Electronic Medical Records. We calculated descriptive statistics, summarizing categorical variables using frequencies and percentages. Numerical variables were summarized using means and standard deviations for normally distributed, and median and interquartile ranges for skewed variables. We recruited 18,210 women living with HIV. Eighty-one percent (14,777/18,210) had their data extracted from the EMR. Data of 10,996 women were analysed. The mean age was 37.4 ± 7.2 years, with 85% in age groups ≥30-39 years. The median time since HIV diagnosis was 8 years (IQR 3-11 years) while the median length of time on ART was 6 years (IQR 3-10 years). For women who had a record of WHO clinical staging and most current viral load, majority (80%) were in WHO stage 1 while two thirds (68.0%) had viral load of <20 copies/mm3. Almost all women (94%) were on first-line antiretrovirals, with none on the third-line regimen. This unique cohort in Nigeria that will provide researchers with a platform to propose and answer several research questions about the health of women and infants providing policymakers with information on maternal and child health in Nigeria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".