Multiplexed technologies for sexually transmitted infections: global evidence on patient-centered and clinical health outcomes
Bibliographic record
Abstract
Introduction Conventional care packages around screening for sexually transmitted infections (STIs) entail multiple clinic visits and precipitate losses to follow-up. To prevent these losses, multiplexed technologies for STIs (immunochromatographic tests/devices/assays and molecular assays that can screen multiple pathogens or multiple strains of one STI) can yield same-day results in a single visit. Research evidence of patient-centred (preference, satisfaction) and clinical health outcomes (feasibility, case positivity, uptake, impact) has not been synthesised. We conducted a systematic review to fill this gap. Methods For the period 2009–2020, two independent reviewers searched PubMed and Embase, retrieved 4440 citations and abstracted data from 42 relevant studies. Results Of 42 studies, 10 (23.8%) evaluated multiplexed immunochromatographic and 32 (76.2%) molecular assays. Outcomes were reported as follows: preference (n=3), satisfaction (n=2), uptake (n=1), feasibility (n=2), case positivity (n=42) and impact (n=11). Screened populations included various at-risk groups. A majority (86.1%–92.4%) of participants preferred (60.2%–97.2%) multiplexed technologies (over conventional testing). Compared with conventional lab-based testing, test uptake improved by 99.4% (hepatitis C), 99.6% (Trichomonas vaginalis), 78.6% (hepatitis B) and 42.0% (HIV). Varying case positivities were documented depending on populations screened: HIV (1.8%–29.3%), hepatitis B (1.1%–23.9%), hepatitis C (0.5%–42.2%), Chlamydia trachomatis (2.8%–30.2%), Neisseria gonorrhoeae (0.0%–30.3%) and T. vaginalis (0.0%–32.7%). Regarding impact, 70.0%–100.0% of screened participants were linked to care, with result turnaround times ranging from 14 min (immunochromatographic assays) to 300 min (molecular assays). Conclusions Compared with conventional lab-based testing, rapid multiplexed technologies were preferred by testees and led to quicker turnaround times for many STIs yielding same-day results thereby allowing to initiate rapid linkages to care. They were further shown to be highly feasible and impactful for detection and treatment facilitation. Based on these promising results, multiplexed technologies offer potential to screen at-risk populations to reduce onward STI transmission worldwide.
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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 | 0.044 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".