O3-S1.06 Diagnostic accuracy of rapid point-of-care tests to detect syphilis: a meta-analyses
Bibliographic record
Abstract
Background The World Health Organization estimates that in 2006, there were 12 million new cases of syphilis. In developing countries, there is a lack of proper screening due to limited laboratory services and long distances from clinics. In developed countries, there is limited access to care among hard-to-reach populations. In this context of disconnect with the health care system, point of care (POC) tests have proven to be an invaluable resource, yet their accuracy needs to be established in order to justify their use. Method Electronic databases were searched from 1 January 1980 to 24 September 2010 for articles evaluating syphilis POC tests. Data were extracted and a second reviewer independently reviewed a subset of the articles. Subgroups were made according to the index test, the sample tested, and reference standard employed. Pooled sensitivity and specificity were calculated using Hierarchical Summary Receiver Operating Characteristic Curve. Adjustments were made to account for imperfect reference standards. Results 30 (47%) from 64 full text articles assessed articles were included in the meta-analysis. The most common kits evaluated were Determine, Bioline, Syphicheck, and Visitect in whole blood and sera samples. Using a Treponema Pallidum (TP) specific reference standard, in sera, the Determine test was the most accurate with a pooled sensitivity of 98.43% (96.03, 99.94) and a specificity of 97.74% (96.38, 98.92). In whole blood, Bioline was the most accurate with a sensitivity of 87.70% (84.78, 90.58) and a specificity of 99.07% (98.50, 99.59). The sensitivity of Determine and Visitect were lower when using whole blood than when using serum. When we adjusted for imperfect reference standards, the pooled parameters of accuracy improved when compared to pooled accuracy under the assumption of a perfect reference standard. Conclusions Determine with high sensitivity and Bioline with high specificity appeared to perform the best of the tests studied. Higher accuracy in serum warrants the use of serum rather than whole blood wherever possible. Confirmation with non-TP specific reference standard are required to confirm whether the infection is active or treated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".