Performance evaluation of existing immunoassays for Clonorchis sinensis infection in China
Bibliographic record
Abstract
BACKGROUND: Clonorchiasis ranks among the most important food-borne parasitic diseases in China. However, due to low compliance to traditional fecal examination techniques in the general population and medical personnel, immunodiagnosis is expected. This study evaluated, in parallel, the performance of four immunodiagnostic kits detecting clonorchiasis in China. RESULTS: A bank with 475 sera was established in this study. Except for the low performance of the kit detecting IgM, the other three kits detecting IgG showed sensitivities ranging from 81.51% (194/238) to 99.16% (236/238). Higher sensitivity was presented in heavy infection intensity [89.47% (68/76) to 100% (76/76)]. Among the four kits, the overall specificity varied from 73.42% (174/237) to 87.34% (207/237). It was observed that the specificity was lower in the sera of the participants living in clonorchiasis-endemic areas but without any parasite infection [67.5% (81/120) to 90% (108/120)], as compared to those from the non-endemic area [94% (47/50) to 98% (49/50)]. The cross-reaction rate varied from 14.93% (10/67) to 31.34% (21/67). Youden's index was -0.022, 0.689, 0.726, and 0.802 for kits T1, T2, T3 and T4, respectively. Repeatability was high in all four kits. CONCLUSIONS: Three immunodiagnosis kits targeting IgG antibody had high performance on detecting chronic Clonorchis sinensis infection, but that detecting IgM antibody had not. The kits detecting IgG antibody also showed high sensitivity in heavy infection intensity. Research on immunological diagnosis of clonorchiasis is expected to be strengthened to improve the sensitivity in light infection and specificity.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".