Diagnostic Value Of Turbid Urine In Urogenital Schistosomiasis In Rural Chad Affected By Climate Change Effects: Case Of The Lake Chad Region
Bibliographic record
Abstract
ABSTRACT Introduction Urogenital schistosomiasis remains a public health issue in sub-Saharan Africa. Diagnosis traditionally relies on detecting eggs in urine or identifying haematuria. The clinical relevance of urine turbidity remains poorly studied. Objective To assess the relevance of the macroscopic appearance of urine, particularly turbidity, as a presumptive indicator of urogenital schistosomiasis. Methods A cross-sectional study was conducted with 299 participants in Ngouri, Chad. Urine appearance was visually assessed and compared with urine dipstick results for haematuria detection. Results Cloudy urine was observed in 20.4% of samples. Among these, 95.1% tested positive on the dipstick, compared to only 16.9% of samples with normal appearance (p < 0.001). Cloudy urine thus appears to be a strong predictive marker. Conclusion In low-resource settings, visual urine assessment combined with symptom analysis can help guide screening and support presumptive treatment. However, the lack of parasitological confirmation remains a significant limitation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".