LBP-1.10 Misclassification of syphilis cases using a reactive enzyme immunoassay and reactive RPR algorithm alone for diagnosis
Bibliographic record
Abstract
Background Recent recommendations propose that samples dually reactive by a syphilis enzyme immunoassay (sEIA) and RPR be reported as positive for syphilis without confirmatory testing. Methods Samples from 1 September 2007 to 19 March 2011 testing reactive by sEIA and tested by INNO-LIA (IL) were extracted from the Alberta Provincial Laboratory for Public Health's DIAL (Data Integration for Alberta Laboratories), a web based application. Syphilis testing history was reviewed for all patients with a reactive sEIA and reactive RPR and negative (NEG) or indeterminate (IND) result by IL. Syphilis infection was defined by a positive confirmatory test (majority IL; a few TPPA and FTA-ABS). The significance of RPR titres in patients NEG/IND by IL with or without evidence of syphilis infection was analysed using χ2test. Median standard cut-offs (s/co) for the sEIA were compared using the Mann–Whitney U test. Results 6195 samples from 4695 patients with reactive sEIA were also tested by IL: 15 samples (0.2%) had no reported RPR result, 4232 (48.3%) were non-reactive by RPR, and 1948 (31.4%) samples from 1753 patients were reactive by RPR. 72 (4.1%) of the 1753 patients with reactive RPR had at least one specimen tested NEG/IND by IL (Abstract LBP-1.10 figure 1). 3 of the 72 patients (4.2%) had a serological history of syphilis infection not recognised initially, 15 (20.8%) had no follow-up testing, 23 (31.9%) had a subsequent positive IL, and 31 (43.1%) did not demonstrate syphilis infection on follow-up testing. For the 31 patients with no serological evidence of syphilis infection, 24 remained NEG/IND by IL and seven tested negative by sEIA on follow-up. Overall, 31 patients (1.7%) would have been misclassified as infected based upon an algorithm of dually reactive sEIA and RPR without confirmatory testing. 14.7% of samples that tested negative or IND for IL in confirmed syphilis had RPR titres =1:8 as compared to 20.0% of samples from patients without syphilis on follow-up serology (p=0.5). The median s/co of the screening sEIA for samples from the patients who were infected (4.8, range: 1.0–24.20) differed from the s/co (2.6, range: 1.0–12.4) for those uninfected (p<0.05). Conclusions In our experience, 1.7% (31 patients) would have been misclassified as a case of syphilis if a third confirmatory test for syphilis had not been conducted. Additional evaluation of syphilis testing algorithms is warranted before a two test algorithm is widely employed. Abstract LBP-1.10 Figure 1 Misclassification syphilis cases
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".