A prospective multi-site study to evaluate the performance and usability of an oral fluid-based HIV self-test in Canada
Bibliographic record
Abstract
BACKGROUND: Blood and oral fluid-based HIV self-tests are important for reaching the undiagnosed living with HIV. The study objectives were to evaluate the oral fluid-based OraQuick® HIV Self-Test (HIV-ST) performance in comparison to laboratory reference testing; determine if laypersons can correctly perform the HIV-ST; document if intended users can successfully interpret pre-made contrived positive, negative, and invalid results; and document if intended users can understand the key messages in the product labeling. METHODS: This prospective study enrolled consenting adult intended users of HIV self-testing from six community health centres in four Canadian provinces between June 2022 and January 2024. Positive and negative agreement was determined by comparing the results of the HIV self-tests with the results of the laboratory-based "gold standard" Abbott Alinity HIV Antigen/Antibody Combo test. Descriptive statistics were used to summarize usability self-test procedure steps. RESULTS: Overall, 951 participants were recruited and consented with 911 available for all analyses. With respect to sociodemographics: 84% of participants were between 18-45 years of age, 73% had at least a college education, 48% were Cis-male, 45% were employed; and 26% identified as White, 23% as African, Caribbean or Black, 5% as Indigenous [First Nations, Métis or Inuit], 33% as Asian, and 6% as LatinX. Primary efficacy analysis on the 911 who completed HIV-ST revealed a single confirmed positive participant and a negative percent agreement of 100% (880/880, 95% CI: 99.9-100%) with the comparator method. For usability determination, the average success rate for "critical" steps for completing the test was 94.1%. Approximately 97% of participants found the instructions easy to follow and 98% of participants reported they would use the test again. Of the 465 participants who interpreted the strong positive, weak positive, negative, and invalid pre-made contrived results, the average of correct interpretations ranged from 59-97% CONCLUSIONS: A licensed oral fluid-based HIV self-test in Canada can present an accurate, easy-to-use, and less invasive alternative to blood-based HIV testing. The addition of an oral-fluid self-test along with the current licensed blood-based HIV self-test could help reach the undiagnosed with HIV in Canada and positively impact HIV testing rates overall by offering individuals a choice of self-testing devices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".