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Comparative Assessment of the Gelatin Particle Agglutination Test and an Enzyme-Linked Immunosorbent Assay for Diagnosis of Strongyloidiasis

2005· article· en· W2127081977 on OpenAlexaff
Paiboon Sithithaworn, T. Janrungsopa, K. Suvatanadecha, Koji Ando, Melissa Haswell

Bibliographic record

VenueJournal of Clinical Microbiology · 2005
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsAssociated Medical Services
FundersNational Research Council of ThailandKentucky Agricultural Experiment Station
KeywordsStrongyloidiasisLatex fixation testAgglutination (biology)MicrobiologyBiologyVirologyMedicineImmunologyHelminthsAntibody

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.071
GPT teacher head0.443
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2005
Admission routes1
Has abstractyes

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