Automated STI/HIV risk assessments: Testing an online clinical algorithm in Ottawa, Canada
Bibliographic record
Abstract
Despite the ongoing transmission of sexually transmitted infections (STIs) and HIV, many people became unable to access testing due to COVID-19. To address this, we created a mail-out HIV self-test kit, which could be delivered without restrictions in our region. The uptake and feedback from this project made us realize that comprehensive STI testing was being sought. To ensure testing occurred correctly—that is, it would be targeted at the persons most affected by STIs/HIV—we automated clinical decision-making. We built this model based on a 2-by-2 matrix that plots the risk of STI/HIV transmission and risk of STI/HIV exposure. The intercept of these two measures classifies a person as low, medium, or high risk. After automating this logic, 16 expert clinicians in STI/HIV care tested this system with over 400 test patient cases and refined the algorithm until it yielded the exact outcomes that these clinicians would offer patients based on guidelines. Findings of interest are that the scale of the y-axis is exponential, in that risk factors for exposure do not climb cumulatively but do so according to a quadratic equation. This helps ensure that testing services are targeted at those who are most inequitably burdened by these infections.
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.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".