Etiology of Genital Ulcer Disease in Dakar, Senegal, and Comparison of PCR and Serologic Assays for Detection of <i>Haemophilus ducreyi</i>
Bibliographic record
Abstract
We used PCR assays to determine the etiology of genital ulcers in patients presenting to a sexually transmitted disease clinic in Dakar, Senegal, and evaluated the ability of two PCR tests (groEL and recD) and two serological tests (adsorption enzyme immunoassay [EIA] and lipooligosaccharide [LOS] EIA) to detect current Haemophilus ducreyi infection. We found that in this population, H. ducreyi, T. pallidum, and herpes simplex virus HSV DNA were detected in 56, 15, and 13% of 39 genital ulcer specimens, respectively, and H. ducreyi DNA was detected in 60% (3 of 5) of samples from ulcerated bubos. Among 40 consecutive patients with genital ulcer disease and with sufficient sample for both PCR assays, the recD and groEL H. ducreyi PCR assays were 83% concordant, with the recD PCR assay detecting six (15%) additional positive specimens and the groEL assay detecting one (3%) additional positive specimen. Compared to PCR, the adsorption EIA and LOS EIA tests had sensitivities of 71 and 59% and specificities of 57 and 90%, respectively, for the diagnosis of current H. ducreyi infection. While these differences in specificity could be due either to previous infection with H. ducreyi or to the detection of cross-reacting antibodies, only 6% of patients from a nearby family planning clinic gave a positive reaction in both the adsorption EIA and LOS EIA assays, indicating that cross-reacting antibodies are not prevalent among clinic attendees in this city. Our studies indicate that the adsorption EIA detects both current and past infection, while the LOS EIA assay is more specific for current infection with H. ducreyi 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".