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<i>Echinococcus granulosus</i> Infection in Spain

2008· article· en· W2088134719 on OpenAlexfundno aff
David Carmena, Luisa P. Sánchez-Serrano, Ismael Barbero-Martínez

Bibliographic record

VenueZoonoses and Public Health · 2008
Typearticle
Languageen
FieldMedicine
TopicParasitic infections in humans and animals
Canadian institutionsnot available
FundersMcGill UniversityWorld Health Organization
KeywordsEchinococcus granulosusEchinococcosisLivestockVeterinary medicineEpidemiologyIncidence (geometry)PrevalenceCystic echinococcosisParasitic diseaseDiseaseZoonotic diseaseInfection rateDisease controlEnvironmental healthBiologyMedicineZoologyPathologyEcologySurgery

Abstract

fetched live from OpenAlex

Cystic echinococcosis (CE) caused by the cestode Echinococcus granulosus is an endemic disease in Spain. Although specific control programmes initiated in the 1980s have led to marked reductions in CE infection rates in Spain, the disease still remains an important human and animal health problem in many regions of the country. Human incidence and livestock (including sheep, cattle, pigs and horses) prevalence data were gathered from national epidemiological surveillance information systems and regional institutions for the period 2000-2005. Additionally, data on the prevalence of E. granulosus infection in dogs were obtained from published literature. The most affected regions were those of the North Eastern, Central and Western parts of the country, (Autonomous Regions of Aragon, Castile-La Mancha, Castile-Leon, Extremadura, Navarre and La Rioja), where human CE incidence rates in the range of 1.1-3.4 cases per 10(5) inhabitants coexist with ovine/bovine CE prevalence rates up to 23%. Control programmes of hydatidosis/echinococcosis should be reinforced in these regions to reduce the prevalence of the disease.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.069
GPT teacher head0.333
Teacher spread0.263 · 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

Citations57
Published2008
Admission routes1
Has abstractyes

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