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Record W2169298842 · doi:10.1017/s0950268809991646

Disease transmission model for community-associated <i>Clostridium difficile</i> infection

2010· article· en· W2169298842 on OpenAlexaffabout
Ainsley Otten, Richard J. Reid‐Smith, A. Fazil, J. Scott Weese

Bibliographic record

VenueEpidemiology and Infection · 2010
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of GuelphPublic Health Agency of Canada
Fundersnot available
KeywordsClostridium difficileClearanceTransmission (telecommunications)DiseaseMedicineRisk of infectionPublic healthIntensive care medicineDisease transmissionInfection controlEnvironmental healthBiologyMicrobiologyVirologyInternal medicinePathologyAntibiotics

Abstract

fetched live from OpenAlex

Participating researchers and public health personnel at a Canadian workshop in 2007, noted considerable gaps in current understanding of community-associated Clostridium difficile infection (CA-CDI), specifically infection sources and risk factors. A disease transmission model for CA-CDI was requested as an initial step towards a risk assessment, to analyse infection sources and risk factors, addressing priority research areas. The developed model contains eight infection states (susceptible, gastrointestinal exposure, colonized, diseased, deceased, clinically resolved colonized, relapse diseased, and cleared) and notes directional transfers between the states. Most published research used focused on hospital-associated C. difficile infection (HA-CDI) and further studies are needed to substantiate the use of HA-CDI knowledge in the transmission of CA-CDI. The aim was to provide a consistent framework for researchers, and provide a theoretical basis for future quantitative risk assessment of CA-CDI.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.074
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.064
GPT teacher head0.354
Teacher spread0.290 · 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 designSimulation or modeling
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

Citations48
Published2010
Admission routes2
Has abstractyes

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