Developing and validating a Bayesian clinical risk prediction model for three sexually transmitted infections in key populations from two Canadian provinces
Bibliographic record
Abstract
Objectives Across Canada, in the last decade, incidence rates of sexually transmitted and blood-borne infections (STBBI) have peaked (syphilis) or plateaued (hepatitis C virus (HCV) and HIV). Key populations (gay, bisexual and other men who have sex with men, trans and gender-diverse people, and people who use injection drugs) are at greater risk for these STBBIs, so correctly predicting risk before screening potentially infected individuals is crucial. We developed and validated a diagnostic clinical risk prediction model (CRPM) estimating HIV, HCV and syphilis risk for two key populations in two Canadian provinces. Methods We used 20 variables and STBBI test results from a cross-sectional study evaluating multiplexed testing (detection of coinfections) in New Brunswick and Quebec (n=400) to develop our CRPM. We randomly split the data into development (n=300) and validation (n=100) datasets using clinic-stratified sampling. We used Bayesian predictive projection with development data to select ranked STBBI predictors. We obtained the ORs of the highest performing submodel measured as area under the receiver operating curve (AUC), sensitivity and specificity with 89% credible intervals (89% CrI) using validation data. Analyses were performed in R (≥V.4.2.3). Results Out of 400 participants, 73 were infected with HIV (n=16), HCV (n=60), and/or syphilis (n=5). An internally validated submodel with two predictors (past drug injection, type of past sexually transmitted infection) displayed the highest AUC (0.79; 89% CrI 0.66 to 0.79), sensitivity (0.85; 89% CrI 0.79 to 0.91) and specificity (0.30; 89% CrI 0.15 to 0.50). The predictor contributing most to STBBI risk was past drug injection (OR=7.62; 89% CrI 4.41 to 13.07). Conclusions This Bayesian-based CRPM is the first to identify high-risk individuals for HIV, HCV and syphilis with an overall good performance that minimises case missing. After additional validation, it could serve as a promising novel tool for prescreening key populations and improve Canadian STBBI multiplexed screening strategies.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".