Comparing the performance characteristics of CSF-TRUST and CSF-VDRL for syphilis: a cross-sectional study
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to determine the performance characteristics of toluidine red unheated serum test on cerebrospinal fluids (CSF-TRUST) as compared to venereal disease research laboratory test on cerebrospinal fluids (CSF-VDRL) for laboratory the diagnosis of neurosyphilis. DESIGN: A cross-sectional study. SETTING: Sexually transmitted infections (STIs) clinics. PARTICIPANTS AND METHODS: CSF and serum samples were collected from 824 individual STD clinic patients who have syphilis and are suspected to progress to neurosyphilis within a 9-month period. CSF-VDRL and CSF-TRUST were performed parallelly on the same day when collected. Treponema pallidum particle agglutination (TPPA) tests were also performed on the CSF and the serum samples, and biochemical analysis of the CSF samples was also performed. RESULTS: The overall agreement between CSF-TRUST and CSF-VDRL was 97.3%. The reactive ratios of the CSF samples were 22.1% by CSF-TRUST and 24.8% by CSF-VDRL, respectively. All CSF-TRUST-reactive cases were reactive in the CSF-VDRL. Twenty-two samples with CSF-TRUST-negative were tested CSF-VDRL-reactive with low titres (1 : 1 to 1 : 4). Over 97% of the double-reactive CSF samples (CSF-VDRL and CSF-TRUST) had an identical titre or a titre within a two-fold difference. The agreement of CSF-TPPA and CSF-VDRL was 71.9%. Similarly, the agreement of CSF-TPPA and CSF-TRUST was 69.2%. CONCLUSIONS: Our results revealed that CSF-TRUST could be used as an option for CSF examination in settings without CSF-VDRL in place.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".