Outcomes of Paediatrics HIV care at the University of Nigeria Teaching Hospital, Ituku-Ozalla, Enugu after ten years of service
Bibliographic record
Abstract
Background: Antiretroviral therapy is associated with improved survival among HIV-infected children. In Nigeria, HIV treatment scale up was extended to children over a decade ago. This poses new challenges of sustained quality care. Aim: To determine the outcomes for HIV infected children and factors that influenced retention in care at the University of Nigeria Teaching Hospital, Ituku/Ozalla, Enugu. Methods: This was a study of HIV -infected children seen between September 2004 and October 2015 and at the Paediatric HIV clinic of the University of Nigeria Teaching Hospital, Ituku Ozalla, Enugu. Data collected include socio-demographics, HAART regimen and outcomes. Data analysis were done with Statistical Package for Social Sciences (SPSS) version 19 (Chicago IL). Results: Five hundred and nineteen of 555 enrolled children with complete data were included in the data analysis. Two hundred and sixty-seven (51.4%) were females. Three hundred and thirty-nine participants (65.3%) were still in care, 12345 (23.7%) had been lost to follow up, or 22 (4.2%) dead while 35 (6.87%) were transferred out to other health facilities or into the adult ART clinic. Factors associated with retention in care were both parents being HIV positive (p<0.0001 commencement of HAART (p<0.0001) and HIV disclosure status of the child (Fisher’s exact Test =0.003).> <0.0001 and HIV disclosure status of the child (Fisher’s exact Test =0.003). Conclusions: About a quarter of our HIV-infected children were lost to follow up. Prompt initiation of HAART and HIV disclosure will positively influence retention in care.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".