Schistosomiasis of the Appendix in a Tertiary Hospital in Northern Nigeria: A 22-Year Review
Bibliographic record
Abstract
BACKGROUND: Schistosomiasis is one of the neglected tropical diseases caused by a trematode, Schistosoma spp, and affects many systems in the body including the gastrointestinal tract. Schistosomiasis of the appendix is a well-recognized disease and presents as a chronic granulomatous inflammation. This study aims to document the frequency and pattern of distribution of schistosomal appendicitis in our environment. MATERIALS AND METHODS: This is a retrospective histopathological review of schistosomiasis of the appendix in the Department of Pathology, Ahmadu Bello University Teaching Hospital, Zaria - Nigeria, between January 1, 1991 to December 31, 2012. RESULTS: Within the study period, there were 1,464 appendectomy specimens histologically examined in the Pathology Laboratory. Thirty of these, representing 2.1%, were diagnosed as schistosomiasis of the appendix. The male:female ratio was 6.5:1 and peak age incidence was in the 20-29 years age group. Abdominal pains, vomiting and fever were seen in 23 (76.7%) and altered bowel motion in seven (23.3%) patients. CONCLUSION: This study showed that schistosomiasis of the appendix is not rare and that its presentation is similar to other forms of appendicitis. There is a need to focus on the prevention of schistosomiasis in order to reduce morbidity among these economically viable age groups.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".