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Record W2043924417 · doi:10.1017/s0950268808001647

Association between indicators of livestock farming intensity and hospitalization rate for acute gastroenteritis

2009· article· en· W2043924417 on OpenAlexafffundabout
Yossi Febriani, Patrick Levallois, Germain Lebel, Suzanne Gingras

Bibliographic record

VenueEpidemiology and Infection · 2009
Typearticle
Languageen
FieldMedicine
TopicViral gastroenteritis research and epidemiology
Canadian institutionsInstitut National de Santé Publique du QuébecCentre hospitalier universitaire de Québec
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsLivestockPoisson regressionAgricultureManureEcological studyConfoundingEnvironmental healthMedicineVeterinary medicineRate ratioAnimal husbandryGeographyEcologyBiologyInternal medicineForestry

Abstract

fetched live from OpenAlex

To evaluate associations between indicators of livestock farming intensity (manure surplus and livestock density) and acute gastroenteritis hospitalization (AGH) rate, we conducted an ecological study on 306 selected agricultural municipalities of Quebec. We estimated the AGH rate for the period 2000-2004 from the Quebec hospital database. Multivariate Poisson regression was used to estimate the strength of association between the farming indicators and AGH with adjustment for confounders. The modifying effect of age and water source was also evaluated. Association between manure and AGH was observed in children, especially those aged 0-4 years for selected zoonotic infections [adjusted hospitalization rate ratio (aHRR) 1.93, 95% CI 1.21-3.09]. The risk ratio was higher for subjects using ground-water source. An increasing HRR trend with each additional level of poultry density was observed in children aged 0-4 years, especially for Salmonella infections. We conclude that livestock farming intensity may be linked to bacterial acute gastroenteritis in children.

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)0.004
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 score0.027
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.343
Teacher spread0.319 · 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

Citations22
Published2009
Admission routes3
Has abstractyes

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