Comparative Assessment of the Gelatin Particle Agglutination Test and an Enzyme-Linked Immunosorbent Assay for Diagnosis of Strongyloidiasis
Bibliographic record
Abstract
The performances of the gelatin particle agglutination test (GPAT) and enzyme-linked immunosorbent assay (ELISA) for the diagnosis of strongyloidiasis with reference to the results of the agar plate culture technique (APCT) were evaluated with samples from 459 individuals from communities in northeast Thailand where strongyloidiasis is endemic. The prevalence of strongyloidiasis in five sample groups determined by GPAT varied between 29.3 and 61.5% (mean, 38.8%). ELISA and APCT, employed concurrently, gave lower prevalence rates of 27.5% (range, 21.6 to 42.1%) and 22.7% (range, 12.7 to 53.8%), respectively. By using APCT as the standard method, the sensitivity of GPAT was generally higher than that of ELISA (81 versus 73%). The specificity of GPAT was slightly lower than that of ELISA (74 versus 86%). The resulting GPAT titers exhibited positive linear relationships with the ELISA values (optical density at 490 nm) (P < 0.05), which suggests that the GPAT titer also reflects the levels of specific antibody comparable to those reflected by the ELISA values. Based on the relative ease and simplicity of use of the technique as well as the acceptable rates of sensitivity and specificity of the test, GPAT is more practical for screening for strongyloidiasis than the conventional ELISA.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".