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Record W2151480067 · doi:10.5539/gjhs.v7n5p196

The Frequency of Enterobius Vermicularis Infections in Patients Diagnosed With Acute Appendicitis in Pakistan

2015· article· en· W2151480067 on OpenAlexvenueno aff
Muhammad Umer Ahmed, Muhammad Bilal, Khurram Anis, Ali Mahmood Khan, Kaneez Fatima, Iqbal Ahmed, Ali Mohammad Khatri

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicAppendicitis Diagnosis and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEnterobiusMedicineAppendixAscaris lumbricoidesMalignancyAppendicitisAcute appendicitisLumen (anatomy)Internal medicineGastroenterologyGeneral surgeryHelminthsImmunologyBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.341
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2015
Admission routes1
Has abstractyes

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