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Analysis of the epidemiological and epizootic situation of alveolar echinococcosis in the world

2025· article· en· W4414866037 on OpenAlexaboutno aff
Ainur A. Zhaksylykova, А.M. Abdybekova, Z.Z. Sayakova, С. Berdiakhmetkyzy, S.А. Kenessary, E.A. Kydyrkhanova

Bibliographic record

VenueHERALD OF SCIENCE OF S SEIFULLIN KAZAKH AGRO TECHNICAL RESEARCH UNIVERSITY Veterinary sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicParasitic infections in humans and animals
Canadian institutionsnot available
Fundersnot available
KeywordsAlveolar echinococcosisEpizooticEpidemiologyEchinococcosisEchinococcusAccidentalZoonotic diseaseEchinococcus multilocularis

Abstract

fetched live from OpenAlex

This review article presents literature data on the distribution of alveolar echinococcosis cases across the world over the past 30 years (1993-2023), statistical data from the WOAH for the past 5 years (2020-2024), and the research results reported by domestic scientists. Alveolar echinococcosis, also known as multilocular echinococcosis, is one of the most dangerous zoonotic parasitic infections. The causative agent, the cestode Echinococcus multilocularis, infects carnivorous animals, small rodents, and humans. Humans, as accidental intermediate hosts, are at high risk for severe complications, including liver and other organ damage. This infection has been recorded in Canada, the USA, Germany, France, Switzerland, Austria, Belgium, the Netherlands, the Czech Republic, Slovakia, Sweden, Denmark, the UK, China, India, Pakistan, Nepal, Bhutan, Iran, Iraq, Mongolia, Lithuania, Latvia, Estonia, Russia, Belarus, the Kyrgyz Republic, and the Republic of Kazakhstan. The World Health Organization classifies this infection as one of the 17 neglected diseases requiring control and elimination by 2050.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.155
GPT teacher head0.449
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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Same venueHERALD OF SCIENCE OF S SEIFULLIN KAZAKH AGRO TECHNICAL RESEARCH UNIVERSITY Veterinary sciencesSame topicParasitic infections in humans and animalsFrench-language works237,207