Rapid point‐of‐care HIV testing in pregnant women: a systematic review and meta‐analysis
Bibliographic record
Abstract
Rapid, point-of-care human immunodeficiency virus (HIV) testing has the potential to enhance strategies to prevent mother-to-child transmission (MTCT) of HIV infection. Rapid tests need minimal laboratory infrastructure and can be performed by health workers with minimal training. In our systematic review and meta-analysis, we aimed to summarize the overall diagnostic accuracy of rapid HIV tests in pregnancy, and outcomes such as acceptability, patient preference, feasibility and impact of rapid testing. We searched four major databases, identified and screened 1377 citations, and included 17 studies that met our eligibility criteria. Analyses of these studies suggested that the overall sensitivity and specificity of blood-based rapid tests was high compared with oral rapid tests. A two-step testing strategy, particularly parallel testing, was found to be superior to single-test strategy in labour and delivery settings. Acceptability of rapid tests and patient preference was variable across studies. Overall, rapid HIV testing was highly accurate compared with conventional tests and offer a clear advantage of enabling the implementation of timely interventions to reduce MTCT of HIV. To improve diagnostic accuracy and to reduce false-positive results, it may be necessary to use two rapid tests during labour and delivery.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".