Detection of <i>Chlamydia trachomatis</i> and <i>Neisseria gonorrhoeae</i> Infections in North American Women by Testing SurePath Liquid-Based Pap Specimens in APTIMA Assays
Bibliographic record
Abstract
The APTIMA COMBO 2 assay, which detects and amplifies rRNA from Chlamydia trachomatis and/or Neisseria gonorrhoeae, is approved for use on ThinPrep liquid-based Pap test specimens. The objective was to determine the clinical utility of the APTIMA assays (APTIMA COMBO 2 assay, APTIMA CT assay for Chlamydia trachomatis, and APTIMA GC assay for Neisseria gonorrhoeae) for screening women during their annual Pap exam, using SurePath liquid-based Pap test specimens. Two cervical samples were collected from 1,615 females attending six clinical sites in North America. A cervical broom sample was processed for cytology, with the residuum aliquoted into an APTIMA specimen transfer kit tube. The second cervical swab sample was put into APTIMA specimen transport medium, and both samples were tested with each APTIMA assay on a direct sampling system. Using a subject-infected status that utilized cervical-swab specimen results from two APTIMA assays, the prevalence was 7.9% for Chlamydia trachomatis and 2.5% for N. gonorrhoeae. For the liquid-based Pap samples, the sensitivities, specificities, positive predictive values, and negative predictive values for Chlamydia trachomatis detection were 85.2%, 99.5%, 93.2%, and 98.7%, respectively, for the APTIMA COMBO 2 assay and 89.1%, 98.7%, 85.7%, and 99.1%, respectively, for the APTIMA CT assay. For N. gonorrhoeae detection, the values were 92.5%, 100%, 100%, and 99.8%, respectively, for the APTIMA COMBO 2 assay and 92.5%, 99.9%, 97.4%, and 99.8%, respectively, for the APTIMA GC assay. The high predictive values support the use of the assays with SurePath liquid-based Pap specimens processed with the APTIMA specimen transfer kit.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".