Urogenital <i>Schistosoma haematobium</i> Cases at the Hospital for Tropical Diseases, London (1998-2018), and Suggested Pragmatic Follow-up Pathway for Non-endemic Settings
Bibliographic record
Abstract
Abstract Background Characteristics of confirmed urogenital Schistosoma haematobium infections and outcomes in non-endemic regions are scarce in the literature and there is a minimal evidence base for appropriate management in this setting. Specific schistosomal urinary and urological complications include risk of hydronephrosis, renal impairment, and malignant transformation. Therefore, approach to follow-up should be robust and systematic. Methods This is a retrospective case-note review of all patients with confirmed S haematobium infection (defined as visible ova in terminal urine and/or histopathological diagnosis on biopsy) at the Hospital for Tropical Diseases (HTD), London, between 1998 and 2018. Outcomes of follow-up were reviewed and formulated into a pragmatic guideline for follow-up of these patients in this setting. Results A majority of the 186 patients with confirmed S haematobium infection presented before 2012. Young, male migrants were at highest risk of complications from chronic infection and were most prone to being lost to follow-up. One patient was referred with squamous cell carcinoma of the bladder found on biopsy with S haematobium infection. Conclusions We put forward a pragmatic pathway for S haematobium investigation and follow-up for patients presenting to nonendemic settings with the current resource capabilities of the United Kingdom.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".