Vaginal Swabs Are the Specimens of Choice When Screening for Chlamydia trachomatis and Neisseria gonorrhoeae: Results From a Multicenter Evaluation of the APTIMA Assays for Both Infections
Bibliographic record
Abstract
BACKGROUND: Vaginal swabs were recently U.S. Food and Drug Administration-cleared for detecting Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (GC) using Gen-Probe Incorporated's APTIMA COMBO2 Assay (AC2). We assessed the APTIMA CT Assay (ACT) for CT, APTIMA GC Assay (AGC) for GC, and AC2 for both organisms using patient- and clinician-collected vaginal swabs. METHOD: Women attending family planning, obstetrics and gynecology, or sexually transmitted disease (STD) clinics had first-catch urines (FCUs), patient-collected vaginal swabs, clinician-collected vaginal swabs, and endocervical swabs tested by ACT, AGC, and AC2. A second endocervical swab and FCU were tested using BD ProbeTec (Becton Dickinson) for CT and GC. We calculated sensitivity and specificity using vaginal swabs to detect CT and GC. RESULTS: Of 1,464 subjects enrolled, 180 had CT and 78 GC. ACT sensitivities and specificities for patient-collected vaginal swabs were 98.3% and 96.5%, respectively; for clinician-collected vaginal swabs, 97.2% and 95.2%, respectively. AGC sensitivities and specificities for patient-collected vaginal swabs were 96.1% and 99.3%, respectively; for clinician-collected vaginal swabs, 96.2% and 99.3%, respectively. AC2 results were similar. If an FCU tested positive for CT or GC, >94% of matching vaginal swabs were positive. Positive endocervical swabs showed slightly less concordance (>90% and >88%, respectively). More infected patients were identified using vaginal swabs than FCUs. With AC2, 171 CT-infected patients were identified using FCUs and 196 using patient-collected vaginal swabs. This difference was more pronounced for CT than for GC. CONCLUSIONS: Vaginal swab specimens allowed sensitive and specific detection of CT and GC in the APTIMA assays. Vaginal swabs identified as many infected patients as endocervical swabs and more than FCUs, and may well be the specimen of choice for screening.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".