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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".