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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it