Opt‐out screening strategy for <scp>HIV</scp> infection among patients attending emergency departments: systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVES: International health agencies have promoted nontargeted universal (opt-out) HIV screening tests in different settings, including emergency departments (EDs). We performed a systematic review and meta-analysis to assess the testing uptake of strategies (opt-in targeted, opt-in nontargeted and opt-out) to detect new cases of HIV infection in EDs. METHODS: We searched the Pubmed and Embase databases, from 1984 to April 2015, for opt-in and opt-out HIV diagnostic strategies used in EDs. Randomized controlled or quasi experimental studies were included. We assessed the percentage of positive individuals tested for HIV infection in each programme (opt-in and opt-out strategies). The mean percentage was estimated by combining studies in a random-effect meta-analysis. The percentages of individuals tested in the programmes were compared in a random-effect meta-regression model. Data were analysed using stata version 12. Quality assessments were performed using the Newcastle-Ottawa Scale. RESULTS: Of the 90 papers identified, 28 were eligible for inclusion. Eight trials used opt-out, 18 trials used opt-in, and two trials used both to detect new cases of HIV infection. The test was accepted and taken by 75 155 of 172 237 patients (44%) in the opt-out strategy, and 73 581 of 382 992 patients (19%) in the opt-in strategy. The prevalence of HIV infection detected by the opt-out strategy was 0.40% (373 cases), that detected by the opt-in nontargeted strategy was 0.52% (419 cases), and that detected by the opt-in targeted strategy was 1.06% (52 cases). CONCLUSIONS: In this meta-analysis, the testing uptake of the opt-out strategy was not different from that of the opt-in strategy to detect new cases of HIV infection in EDs.
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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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".