The Frequency of Enterobius Vermicularis Infections in Patients Diagnosed With Acute Appendicitis in Pakistan
Bibliographic record
Abstract
INTRODUCTION: The main aim of this study was to determine the frequency of Enterobius vermicularis infections and other unique histopathological findings in patients diagnosed with acute appendicitis. MATERIALS: This retrospective study was conducted in a tertiary care hospital of Karachi, Pakistan over a time period of 9 years from 2005 to 2013. The recorded demographic and histopathological data for the 2956 appendectomies performed during this time frame were extracted using a structured template form. Negative and incidental appendectomies were excluded from the study. RESULTS: Out of the 2956 patients diagnosed with acute appendicitis, 84 (2.8%) patients had Enterobius vermicularis infections. Malignancy (n=2, 0.1%) and infection with Ascaris (n=1, 0.1%) was found very rarely among the patients.Eggs in lumen (n=22, 0.7%), mucinous cystadenoma (n=28, 1.0%), mucocele (n=11, 0.4%), lymphoma (n=9, 0.3%), obstruction in lumen (n=17, 0.6%) and purulent exudate (n=37, 1.3%) were also seldom seen in the histopathological reports. CONCLUSION: Enterobius vermicularis manifestation is a rare overall but a leading parasitic cause of appendicitis. Steps such as early diagnosis and regular de worming may help eradicate the need for surgeries.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".