They are likely to be there: using a family-centered index testing approach to identify children living with HIV in Kenya
Bibliographic record
Abstract
In Kenya, only half of children with a parent living with HIV have been tested for HIV. The effectiveness of family-centered index testing to identify children (0-14 years) living with HIV was examined. A retrospective record review was conducted among adult index patients newly enrolled in HIV care between May and July 2015; family testing, results, and linkage to treatment outcomes were followed through May 2016 at 60 high-volume clinics in Kenya. Chi square test compared yield (percentage of HIV tests positive) among children tested through family-centered index testing, outpatient and inpatient testing. Review of 1937 index client charts led to 3005 eligible children identified for testing. Of 2848 (94.8%) children tested through family-centered index testing, 127 (4.5%) had HIV diagnosed, 100 (78.7%) were linked to care, and 85 of those eligible (91.4%) initiated antiretroviral therapy (ART).Family testing resulted in higher yield compared to inpatient (1.8%, p < 0.001) or outpatient testing (1.6%, p < 0.001). The absolute number of children living with HIV identified was highest with outpatient testing. The relative contribution of testing approach to total children identified with HIV was outpatient testing (69%), family testing (26%), and inpatient testing (5%). The family testing approach demonstrated promise in achieving the first two "90s" (identification and ART initiation) of the 90-90-90 targets for children, with additional effort required to improve linkage from testing to treatment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".