Simple questionnaire and urine reagent strips compared to microscopy for the diagnosis of <i>Schistosoma haematobium</i> in a community in northern Ghana
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".