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Record W2020939894 · doi:10.1017/s0950268806006650

The use of randomization tests to assess the degree of similarity in PFGE patterns of <i>E. coli</i> O157 isolates from known outbreaks and statistical space–time clusters

2006· article· en· W2020939894 on OpenAlexafffundabout
David L. Pearl, Marie Louie, Linda Chui, Kathryn Doré, Kristine Grimsrud, S.W. Martin, Pascal Michel, Lawrence W. Svenson, Scott A. McEwen

Bibliographic record

VenueEpidemiology and Infection · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEscherichia coli research studies
Canadian institutionsPublic Health Agency of CanadaProvincial Laboratory of Public HealthAlberta HealthUniversity of Guelph
FundersCanadian Institutes of Health ResearchWellcome Trust
KeywordsPercentileStatisticsInterquartile rangeRandomizationOutbreakStandard deviationSimilarity (geometry)Cluster (spacecraft)ExtrapolationBiologyMathematicsComputer scienceBioinformaticsClinical trialArtificial intelligenceVirology

Abstract

fetched live from OpenAlex

Using isolates from reported cases of Escherichia coli O157 from Alberta, Canada in 2002, we applied randomization tests to determine if cases associated with an outbreak or statistical space-time cluster had more similar pulsed-field gel electrophoresis patterns, based on Dice coefficients, than expected by chance alone. Within each outbreak and space-time cluster, we assessed the mean, median, 25th percentile, 75th percentile, standard deviation, coefficient of variation, and interquartile range of the Dice coefficients of each pairwise comparison among the isolates. To assess the statistical significance of measures of location (e.g. mean) and variation (e.g. standard deviation) we created randomization distributions using all isolates or only isolates from sporadic cases. We determined that randomization tests are an appropriate tool for evaluating the similarity among isolates from cases that have been linked epidemiologically or statistically. We found little difference between using all cases or only sporadic cases when creating our randomization distributions.

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)0.001
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 score0.051
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
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 head0.060
GPT teacher head0.319
Teacher spread0.259 · 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

Citations5
Published2006
Admission routes3
Has abstractyes

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