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 χ 2 test. 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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".