Utility of Diagnostic Imaging in the Diagnosis and Management of Schistosomiasis
Bibliographic record
Abstract
Diagnosis of schistosomiasis is made by demonstration of the parasite ova in stools, urine,and biopsy specimens from affected organs, or presence of antibodies to the different stages of the parasite or antigens circulating in body fluids by serologic techniques. DNA of schistosomes can now also be detected in serum and stool specimens by molecular technique.However, these tests are unable to determine the severity of target organ pathology and resultant complications. Accurate assessment of schistosome-induced morbidities is now made with the use of imaging techniques like ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). US has made major contributions in the diagnosis of hepatosplenic and urinary form of disease. This imaging method provides real time results, is portable (can be carried to the bed side and the field) and is lower in cost than other imaging techniques. Typical findings in hepatosplenic schistosomiasis by US include: hyperechoic fibrotic bands along the portal vessels (Symmer's fibrosis), reduction in the size of the right lobe, hypertrophy of the left lobe, splenomegaly, and ascites. More advanced ultrasound equipment like the colour Doppler ultrasound can characterize portal vein perfusion, a procedure that is critical for the prediction of disease prognosis and for treatment options for complicated portal hypertension. Although CT and MRI are more expensive, are hospital based, and require highly additional specially-trained personnel, they provide more accurate description of the pathology, not only in hepatosplenic and urinary forms of schistosomiasis, but also in the diagnosis of ectopic forms of the disease,particularly involving thebrain and spinal cord. MRI demonstrates better tissue differentiation and lack of exposure to ionizing radiation compared with CT.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".