Syndromic Versus Laboratory-Based Diagnosis of Cervical Infections Among Female Sex Workers in Benin
Bibliographic record
Abstract
BACKGROUND: The syndromic diagnostic approach is the most realistic and cost-effective strategy for controlling sexually transmitted infections (STIs) in the developing world. Its potential advantages should be evaluated. GOAL: The goal of the current study was to examine whether the syndromic approach might diagnose more cases of cervicitis due to Neisseria gonorrhoeae or Chlamydia trachomatis than laboratory tests. STUDY DESIGN: The participants were 481 female sex workers in Benin, screened for STIs and treated on the basis of the clinical findings. They were asked to return to the clinic within 10 days for laboratory test results and appropriate treatment when necessary. RESULTS: The prevalence of cervical infections was 24.5%. In comparison to the gold standard, the sensitivity of the syndromic diagnosis approach for the detection of N gonorrhoeae/C trachomatis infections was 48.3%; that of the locally performed laboratory tests was 74.6%. However, the sensitivity of the laboratory tests dropped to 28.8% when it was taken into consideration that 57.6% of the infected women did not return to the clinic within 10 days. CONCLUSIONS: The syndromic diagnosis approach should continue to be used for female sex workers in Benin because returning for treatment is problematic. Presumptive treatment at their initial visit could be a complement to this approach, given the high prevalence of cervicitis in this population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".