1513. Evaluating the Incremental Accuracy of HIV Self-Test Together with an App-Based Solution: A Secondary Data Analysis of Trial Data
Bibliographic record
Abstract
Abstract Background According to the United Nations Programme on HIV/AIDS (UNAIDS), in 2019, two out of every seven new human immunodeficiency virus (HIV) infections, globally, were among young people aged 15 to 24 years old. HIV self-testing (HIVST) is a convenient strategy that helps increase knowledge of HIV serostatus in the young. In 2012, the Food and Drug Administration (FDA) approved the OraQuick In-Home HIV Test with a reported sensitivity of 92%. Digital supports such as applications (Apps) and websites, in conjunction with oral self-tests, have demonstrated a high feasibility, acceptability, and impact, yet data on accuracy with digital supports remain largely unexplored. Methods We performed a secondary data analysis of a quasi-randomized trial of oral HIVST with HIVSmart! conducted in South African township populations (2017-2019). We hypothesized that HIVSmart! guided interpretation increased test accuracy. We evaluated the diagnostic accuracy of the HIVSmart! guided interpretation of an oral self-test result against the reference standard (dual Elisa and HIV RNA). Stored picture of a self-test result was uploaded by the participants. Results Accuracy data from (n=1489) HIVST participants versus reference standard (2 rapid tests and HIV RNA) demonstrated: Sensitivity: 95.52% (95% CI: 94.48%-96.56%) Specificity: 99.93% (95% CI: 99.79%-100.00%) Positive Predictive Value: 99.22% (95% CI: 98.78%-99.67%) Negative Predictive Value: 99.57% (95% CI: 99.24%-99.90%) Conclusion With the App, we noticed an improved sensitivity to 95.5% (from 92% with self-tests without the use of the App), specificity high at 99%, together with high positive and negative predictive values. Findings demonstrate that Smart-App based digital interpretation removed subjectivity and increased accuracy of test result interpretation, together with recording and storage of data for monitoring purposes. Findings suggest that Smart-App based readers could be useful adjuncts to improve the accuracy estimations of self-tests, translating to increased trust and confidence in self-tests. Disclosures All Authors: No reported disclosures
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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 | 0.056 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".