The application of latent class analysis for diagnostic test validation of chronic Trypanosoma cruzi infection in blood donors
Bibliographic record
Abstract
The main strategy to prevent transfusion-associated Chagas disease is the identification of T. cruzi-infected blood donors by serological screening tests, however there is no perfect serological gold standard. We evaluated an enzyme immunoassay (EIA), an indirect hemaglutination (IHA), and an indirect immunofluorescence (IIF) test for detecting T. cruzi antibodies in Brazilian blood donors. The results were submitted to latent class analysis, and a radioimmunopreciptation (RIPA) test was performed on repeatedly positive samples. Among 1951 donors, 11 (0.56%) were positive by EIA, 6 (0.31%) by IHA and 16 (0.82%) by IIF. Six samples were positive with all tests, while 4 reacted with EIA and IIF. The RIPA was positive in 6 (75.0%), 7 (66.6%), and 4 (54.0%) samples reacting by the EIA, IHA and IIF tests, respectively. The latent class model detected a high sensitivity rate (100%) for the EIA and IIF, and a specificity rate of 99.95% and 99.69% for the EIA and IIF tests, respectively. The probability of being case according to the model was 99.92% when both EIA and IIF were positive, and 100% for the association of EIA, IIF, and IHA.
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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 | 0.034 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".