Improvement of Clinical Algorithms for the Diagnosis of Neisseria gonorrhoeae and Chlamydia trachomatis by the Use of Gram-Stained Smears Among Female Sex Workers in Accra, Ghana
Bibliographic record
Abstract
BACKGROUND: Screening for cervical infection is difficult in developing countries. Screening strategies must be improved for high-risk women, such as female sex workers. GOAL: To evaluate the sensitivity and specificity of screening algorithms for cervical infection pathogens among female sex workers in Accra, Ghana. STUDY DESIGN: A cross-sectional study among female sex workers was conducted. Each woman underwent an interview and a clinical examination. Biologic samples were obtained for the diagnosis of HIV, syphilis, bacterial vaginosis, yeast infection, Trichomonas vaginalis, Neisseria gonorrhoeae, and Chlamydia trachomatis infection. Signs and symptoms associated with cervicitis agents were identified. Algorithms for the diagnosis of cervical infection were tested by computer simulations. RESULTS: The following prevalences were observed: HIV, 76.6%; N. gonorrhoeae, 33.7%; C. trachomatis, 10.1%; candidiasis, 24.4%; T. vaginalis, 31.4%; bacterial vaginosis, 2.3%; serologic syphilis, 4.6%; and genital ulcers on clinical examination, 10.6%. The best performance of algorithms were reached when using a combination of clinical signs and a search for gram-negative diplococci on cervical smears (sensitivity, 64.4%; specificity, 80.0%). CONCLUSIONS: In the algorithms, examination of Gram-stained genital smears in female sex workers without clinical signs of cervicitis improved sensitivity without altering specificity for the diagnosis of cervical infection.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".