Diagnosing sexually transmitted infections in resource‐constrained settings: challenges and ways forward
Bibliographic record
Abstract
INTRODUCTION: Sexually transmitted infections (STIs) remain prevalent and are increasing in several populations. Appropriate STI diagnosis is crucial to prevent the transmission and sequelae of untreated infection. We reviewed the diagnostic accuracy of syndromic case management and existing point-of-care tests (POCTs), including those in the pipeline, to diagnose STIs in resource-constrained settings. METHODS: We prioritized updating the systematic review and meta-analysis of the diagnostic accuracy of vaginal discharge from 2001 to 2015 to include studies until 2018. We calculated the absolute effects of different vaginal flowcharts and the diagnostic performance of POCTs on important outcomes. We searched the peer-reviewed literature for previously conducted systematic reviews and articles from 1990 to 2018 on the diagnostic accuracy of syndromic management of vaginal and urethral discharge, genital ulcer and anorectal infections. We conducted literature reviews from 2000 to 2018 on the existing POCTs and those in the pipeline. RESULTS AND DISCUSSIONS: The diagnostic accuracy of urethral discharge and genital ulcer disease syndromes is relatively adequate. Asymptomatic Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) infections limit the use of vaginal discharge and anorectal syndromes. The pooled diagnostic accuracy of vaginal syndromic case management for CT/NG is low, resulting in high numbers of overtreatment and missed treatment. The absolute effect of POCTs was reduced overtreatment and missed treatment. Findings of the reviews on syndromic case management underscored the need for low-cost and accurate POCTs for the identification, first, of CT/NG, and, second, of Mycoplasma genitalium (MG) and Trichomonas vaginalis (TV) and NG and MG resistance/susceptibility testing. Near-patient POCT molecular assays for CT/NG/TV are commercially available. The prices of these POCTs remain the barrier for uptake in resource-constrained settings. This is driving the development of lower cost solutions. CONCLUSIONS: The WHO syndromic case management guidelines should be updated to raise the quality of STI management through the integration of laboratory tests. STI screening strategies are needed to address asymptomatic STIs. POCTs that are accurate, rapid, simple and affordable are urgently needed in resource-constrained settings to support the uptake of aetiological diagnosis and treatment.
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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 | 0.073 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.023 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".