Bibliographic record
Abstract
Rates of syphilis, including congenital syphilis, have been rising worldwide. Syphilis is particularly difficult to diagnose given its non-specific symptoms and the inability to culture Treponema pallidum on regular media. Therefore, various diagnostic methods and complementary testing algorithms have been devised with the aim of accurately diagnosing syphilis. These diagnostic methods include direct detection techniques, useful in diagnosing early primary syphilis before seroconversion, and serological testing divided into treponemal (TT) and non-treponemal (NTT) tests. TTs detect Treponema pallidum -specific antibodies but cannot differentiate active from past infections, while NTT titres correlate with disease activity. In neonates being evaluated for congenital syphilis, maternal antibody transfer complicates this interpretation, necessitating careful correlation with clinical findings and maternal history. We explore these aforementioned tests with cases highlighting how they can be used and interpreted in different clinical scenarios.
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How this classification was reachedexpand
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 | 0.000 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".