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Record W4361302748 · doi:10.1136/sextrans-2022-055557

Self-sampling strategies (with/without digital innovations) in populations at risk of<i>Chlamydia trachomatis</i>and<i>Neisseria gonorrhoeae</i>: a systematic review and meta-analyses

2023· review· en· W4361302748 on OpenAlexafffundabout
Fiorella Vialard, Apoorva Anand, Cindy Leung Soo, Anna de Waal, Madison McGuire, Sergio Carmona, Marta Fernández-Suárez, Alice Zwerling, Nitika Pant Pai

Bibliographic record

VenueSexually Transmitted Infections · 2023
Typereview
Languageen
FieldImmunology and Microbiology
TopicReproductive tract infections research
Canadian institutionsUniversity of OttawaMcGill UniversityCentre for Advancing Health OutcomesMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsChlamydia trachomatisMedicineNeisseria gonorrhoeaeSampling (signal processing)Observational studyGonorrheaMeta-analysisChlamydiaGynecologyInternal medicineFamily medicineImmunologyBiologyHuman immunodeficiency virus (HIV)

Abstract

fetched live from OpenAlex

BACKGROUND: (GC) resulted in over 200 million new sexually transmitted infections last year. Self-sampling strategies alone or combined with digital innovations (ie, online, mobile or computing technologies supporting self-sampling) could improve screening methods. Evidence on all outcomes has not yet been synthesised, so we conducted a systematic review and meta-analysis to address this limitation. METHODS: We searched three databases (period: 1 January 2000-6 January 2023) for reports on self-sampling for CT/GC testing. Outcomes considered for inclusion were: accuracy, feasibility, patient-centred and impact (ie, changes in linkage to care, first-time testers, uptake, turnaround time or referrals attributable to self-sampling).We used bivariate regression models to meta-analyse accuracy measures from self-sampled CT/GC tests and obtain pooled sensitivity/specificity estimates. We assessed quality with Cochrane Risk of Bias Tool-2, Newcastle-Ottawa Scale and Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS: We summarised results from 45 studies reporting self-sampling alone (73.3%; 33 of 45) or combined with digital innovations (26.7%; 12 of 45) conducted in 10 high-income (HICs; n=34) and 8 low/middle-income countries (LMICs; n=11). 95.6% (43 of 45) were observational, while 4.4% (2 of 45) were randomised clinical trials.We noted that pooled sensitivity (n=13) for CT/GC was higher in extragenital self-sampling (>91.6% (86.0%-95.1%)) than in vaginal self-sampling (79.6% (62.1%-90.3%)), while pooled specificity remained high (>99.0% (98.2%-99.5%)).Participants found self-sampling highly acceptable (80.0%-100.0%; n=24), but preference varied (23.1%-83.0%; n=16).Self-sampling reached 51.0%-70.0% (n=3) of first-time testers and resulted in 89.0%-100.0% (n=3) linkages to care. Digital innovations led to 65.0%-92% engagement and 43.8%-57.1% kit return rates (n=3).Quality of studies varied. DISCUSSION: Self-sampling had mixed sensitivity, reached first-time testers and was accepted with high linkages to care. We recommend self-sampling for CT/GC in HICs but additional evaluations in LMICs. Digital innovations impacted engagement and may reduce disease burden in hard-to-reach populations. PROSPERO REGISTRATION NUMBER: CRD42021262950.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.793
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.406
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations11
Published2023
Admission routes3
Has abstractyes

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