Diagnostic and follow-up performance of serological tests for different forms/courses of alveolar echinococcosis
Bibliographic record
Abstract
Diagnosis of alveolar echinococcosis (AE) is predominantly based on imaging procedures combined with immunodiagnostic testing. In the present study, we retrospectively analyzed the performance of four serological tests (EgHF-ELISA, Em2-ELISA, recEm18-ELISA and Em-Immunoblotting) for initial diagnosis and subsequent monitoring of AE patients. Overall, 101 AE patients were included, grouped according to treatment options and immune status as follows: (A) curative surgical treatment (n = 45 patients), (B) non-radical or palliative surgical treatment (n = 11), (C) benzimidazoles only (n = 20), (D) immunocompromised with radical surgical treatment (n = 11), (E) immunocompromised with benzimidazoles only (n = 4), and finally a group of 10 AE patients (F) that were considered to present so-called “abortive” lesions. Initial (i.e. pretreatment) ELISA-based diagnosis for patients in groups A to E revealed overall diagnostic sensitivities of 95% for EgHF, 86% for Em2, and 80% for recEm18, respectively. Comparatively, the diagnostic sensitivity of Em-Immunoblotting was higher with an overall value of 98%. In group F, only Em-Immunoblotting had an excellent diagnostic sensitivity (100%), whereas the ELISAs had poor sensitivities of 30% (EgHF- and Em2-ELISA) or even 0% (recEm18-ELISA). Serological monitoring of AE patients showed a clear association between a curative development of disease (induced either by surgery or benzimidazole medication) and a negativization in the ELISAs. This effect was most pronounced for the recEm18-ELISA, where 56% negativized following diagnosis/treatment, as compared to 36% for the EgHF-ELISA, and 37% for the Em2-ELISA, respectively. After radical surgery, the mean time until negativization in the recEm18-ELISA was 2.4 years (SD 1.6). This was significantly shorter than the mean 3.9 years (SD 2.5) in those AE patients with non-radical, palliative surgery or ABZ treatment who were able to negativize during the study period ( p = 0.048). Conclusively, Em-Immunoblotting appears as the most sensitive test to diagnose active as well as inactive (“abortive”) AE-cases. The inclusion of the ELISAs completes the initial diagnostic picture and offers valuable additional information. Conversely, recEm18-ELISA appears as the currently best serological tool to monitor a regressive and putatively curative course of AE in treated patients.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.005 | 0.002 |
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".