Treponema-Specific Tests for Serodiagnosis of Syphilis: Comparative Evaluation of Seven Assays
Bibliographic record
Abstract
The diagnosis of syphilis is challenging and often relies on serologic tests to detect treponemal or nontreponemal antibodies. Recently, the Centers for Disease Control and Prevention and the Association of Public Health Laboratories proposed an update to the syphilis serology testing algorithm, in which serum samples are first tested using a treponema-specific test and positive samples are analyzed with a nontreponemal assay. The goal of this study was to compare the performance of seven treponemal assays (BioPlex 2200 syphilis IgG [Bio-Rad, Hercules, CA], fluorescent treponemal antibody [FTA] assay [Zeus Scientific, Raritan, NJ], Treponema pallidum particle agglutination [TP-PA; Fujirebio Diagnostics, Malvern, PA], Trep-Sure enzyme immunoassay [EIA; Phoenix Biotech, Oakville, Ontario, Canada], Trep-Chek EIA [Phoenix Biotech], Trep-ID EIA [Phoenix Biotech], and Treponema ViraBlot IgG [Viramed Biotech AG, Planegg, Germany]) using serum samples (n = 303) submitted to our reference laboratory. In addition to testing with these 7 assays, all samples were tested by a rapid plasma reagin (RPR) assay and a treponemal IgM Western blot assay (Viramed ViraBlot). Compared to the FTA assay as the gold standard, the evaluated treponemal tests demonstrated comparable levels of performance, with percent agreement ranging from 95.4% (95% confidence interval, 92.3 to 97.3) for the Trep-Sure EIA to 98.4% (96.1 to 99.4) for the Trep-ID EIA. Compared to a "consensus of the test panel" (defined as at least 4 of 7 treponemal tests being in agreement), the percent agreement ranged from 95.7% (92.7 to 97.5) for Trep-Sure to 99.3% (97.5 to 99.9) for Trep-ID. These data may assist clinical laboratories that are considering implementing a treponemal test for screening or confirmatory purposes.
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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 | 0.027 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".