Evidence for optimal HIV testing intervals in HIV-negative individuals from various risk groups: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Evidence-based recommendations for HIV testing are essential for health care providers. However, it is unclear whether there is sufficient evidence to support recommendations for HIV testing frequencies in a variety of HIV risk groups. OBJECTIVE: The aim of this document is to outline the methodological protocol of a systematic review that would gather evidence for the optimal frequency of HIV testing among individuals in various HIV risk groups with respect to personal and public health outcomes and cost-effectiveness. METHODS: This protocol adheres to the PRISMA-P reporting items, and the review is registered with PROSPERO. The target population includes individuals who may have undiagnosed HIV infection. Different frequencies of HIV testing will be compared and outcomes to do with personal and public health, patient values/preferences and costs will be examined. The search strategy will encompass searches in MEDLINE/Pubmed, Scopus, Embase, Cochrane, PsychINFO, and EconLit, as well as grey literature sources. Articles will be screened by title/abstract, and subsequently by full-text, in duplicate. Extraction of pertinent data from the screened references will be carried out by one reviewer and verified by a second. Multiple critical appraisal tools will be used to assess individual study quality, and the GRADE approach will be used to appraise the overall quality of the evidence. Data will be synthesized narratively, and the results will be published in a peer-reviewed journal. DISCUSSION: This systematic review, designed with extensive input from content experts, will help to identify key evidence to inform recommendations for HIV testing frequency.
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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 | 0.127 | 0.123 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.070 | 0.013 |
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".