HIV risk assessment tools for identifying individuals who could benefit from pre-exposure prophylaxis: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Pre-exposure prophylaxis (PrEP) is a highly effective, safe and acceptable intervention for preventing HIV infection. However, identifying individuals who could best benefit from PrEP remains a significant challenge. Existing HIV risk assessment tools vary in performance depending on context. This systematic review aims to synthesise evidence on their diagnostic performances to predict incident HIV infection. METHODS AND ANALYSIS: This protocol is informed and reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Protocols. We will search MEDLINE (Ovid), Embase (Ovid) and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases (January 1998-May 2024) for observational and relevant interventional studies assessing the diagnostic performance of HIV risk tools to predict incident HIV for PrEP eligibility. There will be no restrictions on study language or location. Two reviewers will conduct the search, data extraction and risk of bias assessment using the Johanna Briggs Institute Critical Appraisal Checklist for Diagnostic Studies. Standardised templates will be used in Covidence for data extraction. We will conduct a meta-analysis if appropriate, otherwise, a narrative review. We will use the PRISMA guidelines to guide reporting. ETHICS AND DISSEMINATION OF RESEARCH: Ethical approval is not required as data is publicly available. This review will inform updates to Canadian HIV PrEP guidelines and guide healthcare professionals in using HIV risk assessment tools for identifying PrEP candidates. Findings will be presented at guideline panel meetings and submitted for publication in a peer-reviewed journal and conferences. PROSPERO REGISTRATION NUMBER: CRD42024543975.
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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 | 0.099 | 0.102 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.014 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.014 |
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".