Exploring the diagnostic accuracy of an HIV self-test optimized by a digital app-based solution: Results from a secondary data analysis of a field trial in South Africa
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: To reach UNAIDS 95-95-95 targets, digital HIV self-testing (HIVST) strategy aided by applications, platforms, and readers can engage young people and adults living with undetected HIV infection. Evidence on its acceptability, feasibility, impact exists, yet accuracy data are limited. METHODS: A secondary data analysis of a quasi-RCT of digital HIVST in South Africa was performed. We hypothesized app-guided digital interpretation of oral self-test enhanced test accuracy. We compared accuracy between digital HIVST supervised vs. unsupervised (with/without healthcare worker). Self-test results were interpreted and uploaded by participants, compared using computer vision technology, against lab reference standard by trained healthcare professionals. RESULTS: 1513 digital HIVST participants reported pooled Sensitivity (Sn) = 95.52% (95% CI, 94.48%-96.56%); Specificity (Sp): 99.93% (95% CI, 99.79%-100.06%); Positive predictive value (PPV): 99.22% (95% CI, 98.78%-99.67%); Negative Predictive Value (NPV): 99.57% (95% CI, 99.24%-99.90%). 565 participants on supervised digital HIVST, reported a pooled Sn: 93.65% (95% CI, 91.64-95.66); Sp: 100.00% (95% CI, 100.00-100.00); PPV: 100.00% (95% CI, 100.00-100.00); NPV: 99.21% (95% CI, 98.48-99.94). 968 unsupervised digital HIVST participants, reported a pooled Sn: 97.18% (95% CI, 96.13-98.24); Sp: 99.89% (95% CI, 99.67-100.10); PPV: 98.57% (95% CI, 97.82-99.33); NPV: 99.77% (95% CI, 99.47-100.08). Non-digital HIVST vs. study digital HIVST data at 5% significance level - Sn: chi = 0.6495, p-value = 0.4203, Sp: chi = 0.3831, p-value = 0.5259. Supervised vs. unsupervised HIVST at 5% significance level - Sn: chi = 0.973, p-value = 0.3237, Sp: chi = 0.527, p-value = 0.4449. CONCLUSIONS: Digital HIVST improved interpretation of test results, increased accuracy and predictive value estimations (upper limit 98%-100%), removing subjectivity. Unsupervised digital HIVST users performed better than supervised. Digital HIVST results can potentially signal a rapid triage to therapy or prevention pathways, while awaiting lab confirmation. Findings have implications for scale up of digital HIVST initiatives in global settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it