Dataset of the Konga community-based cluster-randomized trial in Tanzanian children with high HIV viral loads
Bibliographic record
Abstract
This dataset contains data from a cluster-randomized controlled trial to evaluate the effectiveness of a community-based intervention (Konga model) for viral load suppression among children living with HIV in Simiyu, Tanzania. Children aged 2‒14 years with a viral load >1,000 cells/mL were randomly assigned to 15 treatment and 30 control clusters based on their area of residence. The intervention included adherence counseling, psychosocial support, and screening for comorbidities. Viral load was measured at baseline and 6 months later. We compared the mean viral loads of participants before and after the intervention. The 82 participants had a mean age of 9 years and a baseline median viral load of 13,150 copies/mL. After the study, the intervention group had significantly higher adherence (92%) than the control group (80%). After adjusting for baseline viral load, the intervention explained 4% of the viral load variation. This trial showed significant benefits of the Konga model. We recommend conducting similar trials elsewhere to confirm the generalizability of the intervention, so that it can be implemented elsewhere Further, we believe that this data will be of interest to the readership of your repository because our data increases our current understanding of the social dimensions of HIV in an African context and provides recommendations related to improving HIV care, particularly for children
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".