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Record W2107349610 · doi:10.1017/s095026881400123x

The association between campylobacteriosis, agriculture and drinking water: a case-case study in a region of British Columbia, Canada, 2005–2009

2014· article· en· W2107349610 on OpenAlexafffundabout
Eleni Galanis, Sunny Mak, Michael Otterstatter, Marsha Taylor, Marc Zubel, Tim K. Takaro, Margot Kuo, Pascal Michel

Bibliographic record

VenueEpidemiology and Infection · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsSimon Fraser UniversityPublic Health Agency of CanadaFraser HealthBC Centre for Disease ControlUniversity of British Columbia
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsCampylobacteriosisOdds ratioEnvironmental healthOddsConfidence intervalMedicineGeographyLogistic regressionCampylobacterBiologyInternal medicine

Abstract

fetched live from OpenAlex

We studied the association between drinking water, agriculture and sporadic human campylobacteriosis in one region of British Columbia (BC), Canada. We compared 2992 cases of campylobacteriosis to 4816 cases of other reportable enteric diseases in 2005-2009 using multivariate regression. Cases were geocoded and assigned drinking water source, rural/urban environment and socioeconomic status (SES) according to the location of their residence using geographical information systems analysis methods. The odds of campylobacteriosis compared to enteric disease controls were higher for individuals serviced by private wells than municipal surface water systems (odds ratio 1·4, 95% confidence interval 1·1-1·8). In rural settings, the odds of campylobacteriosis were higher in November (P = 0·014). The odds of campylobacteriosis were higher in individuals aged ⩾15 years, especially in those with higher SES. In this region of BC, campylobacteriosis risk, compared to other enteric diseases, seems to be mediated by vulnerable drinking water sources and rural factors. Consideration should be given to further support well-water users and to further study the microbiological impact of agriculture on water.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.040
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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

Citations27
Published2014
Admission routes3
Has abstractyes

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