Evaluation of portable microscopic devices for the diagnosis of <i>Schistosoma</i> and soil-transmitted helminth infection
Bibliographic record
Abstract
The diagnosis of parasitic worm (helminth) infections requires specialized laboratory settings, but most affected individuals reside in locations without access to such facilities. We tested two portable microscopic devices for the diagnosis of helminth infections in a cross-sectional survey in rural Côte d'Ivoire. We examined 164 stool samples under a light microscope and then re-examined with a commercial portable light microscope and an experimental mobile phone microscope for the diagnosis of Schistosoma mansoni and soil-transmitted helminths. Additionally, 180 filtered urine samples were examined by standard microscopy and compared with the portable light microscope for detection of Schistosoma haematobium eggs. Conventional microscopy was considered the diagnostic reference standard. For S. mansoni, S. haematobium and Trichuris trichiura, the portable light microscope showed sensitivities of 84.8%, 78.6% and 81.5%, respectively, and specificities of 85.7%, 91.0% and 93.0%, respectively. For S. mansoni and T. trichiura, we found sensitivities for the mobile phone microscope of 68.2% and 30.8%, respectively, and specificities of 64.3% and 71.0%, respectively. We conclude that the portable light microscope has sufficient diagnostic yield for Schistosoma and T. trichiura infections, while the mobile phone microscope has only modest sensitivity in its current experimental set-up. Development of portable diagnostic technologies that can be used at point-of-sample collection will enhance diagnostic coverage in clinical and epidemiological settings.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".