S11.2 Which algorithm performs better, screening with a non-treponemal or treponemal test?
Bibliographic record
Abstract
Background A growing number of diagnostic laboratories have recently adopted treponemal EIA tests that permits automation for syphilis screening thus reducing time and labor. This leads to a reverse sequence approach of screening in which an EIA is performed first, followed by testing of reactive sera with a non-treponemal test. The province of Quebec implemented two revised algorithms for syphilis testing on 1 February 2010. The first algorithm (Algo 1) is adapted for low throughput laboratories who initiate testing with a non-treponemal test while the second (Algo 2), which is adapted for high throughput settings, follows the reverse sequence approach. Using these recently implemented algorithms in Quebec, the performance the reverse sequence algorithm will be discussed. Methods The performance algorithms 1 and 2 has been evaluated with a retrospective analysis of all sera sent by diagnostic laboratories to our reference laboratory for treponemal confirmation between 1 February 2010 and 31 January 2011. Positive sera by both EIA and RPR were not submitted for confirmation. Results A total of 3662 sera were sent for confirmation during the study period. Only sera from patients not known to have a previous positive treponemal test were analysed. Among the 929 RPR positive or indeterminate sera screened by Algo 1, only 315 (34%) were positive by TP-PA. Among the 904 EIA positive/RPR negative sera screened by Algo 2, 525 (58%) were positive, 333 (37%) were negative and 46 (5%) were indeterminate by TP-PA. The TP-PA negative or indeterminate sera were further tested using a line immunoassay. Among these 379 sera, 35 (9%) were positive and 108 (28%) were indeterminate by line immunoassay. The overall proportion of false positive EIA when reflex RPR test is negative (Algo 2) was 38% compared to a proportion of 66% (614/929) of false positive results when RPR is used as the first screening assay (Algo 1). Conclusion The higher rate of false positive when sera are screened with Algo 1 can be explained by a low prevalence setting. The high rate of false positive EIA when RPR test is negative (Algo 2) confirms the need to reflexively test all such sera with at least a second treponemal test. Although most EIA positive/RPR negative/TP-PA negative sera truly are false positive EIA results, a second treponemal confirmatory test helps identifying more true-positive EIA positive/RPR negative sera, though more data are needed to generally recommend this approach.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.123 | 0.034 |
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".