MétaCan
Menu
Back to cohort
Record W2016287502 · doi:10.5539/gjhs.v6n3p9

Intestinal Parasitic Infestation in Combatants and Their Families: A Hospital-Based Study in Mid-Western Regional Police Hospital, Nepal

2014· article· en· W2016287502 on OpenAlexvenueno aff
Damodar Paudel, Myo Nyein Aung, Bindhya Sharma, Thin Nyein Nyein Aung, Saiyud Moolphate

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasites and Host Interactions
Canadian institutionsnot available
FundersFaculty of Tropical Medicine, Mahidol UniversityMahidol University
KeywordsAscaris lumbricoidesInfestationGiardia lambliaVeterinary medicineEntamoeba histolyticaHelminthsMedicineEntamoeba coliAbdominal painHymenolepis nanaBiologySurgeryPathologyImmunology

Abstract

fetched live from OpenAlex

OBJECTIVE: To find out the scenario of intestinal parasitic infestation in combatants and their families in the setting of Mid-Western Regional Police Hospital (MWRPH), Nepal. STUDY DESIGN: Cross-sectional study. METHODS: All 2005 patients presented with the complaint of abdominal pain, diarrhoea, frequent defecation, blood in stool, or black stool from August 2007 to February 2011 were offered a stool examination. About 10g of fresh stool was collected in a clean, dry bottle. Two slides from each specimen were examined applying light microscope in 10 and 40 uvf at Banke, Nepalgunj hospital laboratory. RESULT: Among 2005 patients, 928 (46.28%) were infested with either helminths and/or protozoa. 96% were single infestation. The most common infestation was Ascaris lumbricoides (48.06%) and the second was hook worm (18.97%). Most common protozoal infestations were Entamoeba histolytica (12.92%) and Giardia lamblia (9.49%). Helminthic infestations peaked in cool months and protozoal infestations were rather steady throughout the year. CONCLUSION: Very high parasitic infestation in least developed mid- western Nepal may need urgent public health intervention.

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)0.001
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 score0.004
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.351
Teacher spread0.330 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicParasites and Host InteractionsFrench-language works237,207