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Simple questionnaire and urine reagent strips compared to microscopy for the diagnosis of <i>Schistosoma haematobium</i> in a community in northern Ghana

2012· article· en· W1636094416 on OpenAlexaff
Isaac I. Bogoch, Jason R. Andrews, Richard Kobina Dadzie Ephraim, Jürg Utzinger

Bibliographic record

VenueTropical Medicine & International Health · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsSchistosoma haematobiumUrineUrine sampleMedicineSchistosomiasisImmunologyHelminthsInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the utility of a simple questionnaire and urine reagent strip testing for the rapid diagnosis of Schistosoma haematobium in rural northern Ghana. METHODS: Cross-sectional parasitological and questionnaire survey in a community in northern Ghana. Participants provided two urine specimens that were examined under a microscope using a centrifugation method. The first urine sample was additionally subjected to reagent strip testing. A short questionnaire was administered to all participants. RESULTS: Microscopy of urine samples obtained from 208 individuals aged 1-77 years revealed an S. haematobium prevalence of 6.8%. The presence of any blood or protein on a urine reagent strip was 100% and 42% sensitive, and 93% and 80% specific for S. haematobium diagnosis. Questionnaires were completed by 198 individuals. Self-reported haematuria showed a sensitivity of 53% and a specificity of 85%. A dichotomous two-question panel was helpful in S. haematobium diagnosis, with working and playing near the river significantly associated with S. haematobium infection (P < 0.001). CONCLUSION: The use of urine reagent strips, coupled with questions pertaining to water contact patterns, might be considered for point-of-contact diagnosis of S. haematobium where microscopy is unavailable.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.042
GPT teacher head0.386
Teacher spread0.344 · 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

Citations38
Published2012
Admission routes1
Has abstractyes

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