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Record W2136948430 · doi:10.1017/s0950268805003687

Spatial and temporal epidemiology of sporadic human cases of Escherichia coli O157 in Scotland, 1996–1999

2005· article· en· W2136948430 on OpenAlexaff
Giles Innocent, D. J. Mellor, Scott A. McEwen, William J. Reilly, Jordan Smallwood, Mary E. Locking, Darren J. Shaw, Pascal Michel, D. Taylor, W.B. Steele, George J. Gunn, H.E. Ternent, Mark Woolhouse, S. W. J. Reid

Bibliographic record

VenueEpidemiology and Infection · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of Guelph
FundersEconomic and Social Research CouncilJoint Information Systems CommitteeUniversity of GlasgowDepartment for Environment, Food and Rural Affairs, UK GovernmentWellcome Trust
KeywordsDemographyGeographyEpidemiologyPopulationCluster (spacecraft)Spatial epidemiologyPopulation densityVeterinary medicineRural populationMedicinePathology

Abstract

fetched live from OpenAlex

In Scotland, between 1995 and 2000 there were between 4 and 10 cases of illness per 100000 population per year identified as being caused by Escherichia coli O157, whereas in England and Wales there were between 1 and 2 cases per 100000 population per year. Within Scotland there is significant regional variation. A cluster of high rate areas was identified in the Northeast of Scotland and a cluster of low rate areas in central-west Scotland. Temporal trends follow a seasonal pattern whilst spatial effects appeared to be distant rather than local. The best-fit model identified a significant spatial trend with case rate increasing from West to East, and from South to North. No statistically significant spatial interaction term was found. In the models fitted, the cattle population density, the human population density, and the number of cattle per person were variously significant. The findings suggest that rural/urban exposures are important in sporadic infections.

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)0.002
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 score0.020
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.052
GPT teacher head0.300
Teacher spread0.248 · 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

Citations47
Published2005
Admission routes1
Has abstractyes

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