Performance characteristics of diagnostic assays for schistosomiasis in Ontario, Canada
Bibliographic record
Abstract
Introduction: Due to lower intensity of infection and greater intervals from last exposure, parasitologic detection methods for schistosomiasis are poorly sensitive in non-endemic areas, challenging accurate diagnosis. Methods: We evaluated parasitologic versus indirect detection methods for schistosomiasis. We included specimens submitted for Schistosoma serology, and stool for ova and parasite microscopy. Three real-time PCR assays targeting Schistosoma mansoni and S. haematobium were performed. Primary outcome measures were sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), where both microscopy and serology were the composite reference standard against serum PCR. Results: Of 8168 serum specimens submitted for Schistosoma serology, 638 (7.8%) were reactive and 6705 (82.1%) were non-reactive. Of 156,771 stool specimens submitted for ova and parasite testing, 46 (0.03%) were positive for eggs of S. mansoni. Four (0.5%) urine specimens were positive for eggs of S. haematobium. Combined serum PCRs targeting S. mansoni had a sensitivity and specificity of 27.8% (95% CI = 18.3–39.1%) and 100% (95% CI = 83.9–100%), respectively, with PPV of 100% (95% CI = 100%) and NPV of 26.9% (95% CI = 24.3–29.7%). The one serum sample positive for S. haematobium was also detectable by our S. haematobium PCR. No cross-reactivity was observed for all three PCR assays. Conclusions: Although serology is highly sensitive, parasitologic tests signify active infection, but are limited by low population-level sensitivity, particularly in non-endemic settings. Although serum PCR offered no performance advantage over stool microscopy, its role in diagnostic parasitology should be pursued due to its high-throughput and operator-independent nature.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".