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Record W2792364982 · doi:10.1093/jcag/gwy009.034

A34 TRENDS AND PREDICTORS OF CLOSTRIDIUM DIFFICILE INFECTION AMONG THE CHILDREN OF MANITOBA: A POPULATION-BASED STUDY

2018· article· en· W2792364982 on OpenAlexaffabout
Zoann Nugent, B. Nancy Yu, Lisa M. Lix, Laura E. Targownik, Çharles N. Bernstein, Harminder Singh

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineClostridium difficilePopulationLogistic regressionDiarrheaPediatricsDemographyInternal medicineEnvironmental healthAntibiotics

Abstract

fetched live from OpenAlex

Clostridium difficile infection (CDI) is a significant cause of morbidity and mortality in both children and adults. Clostridium difficile causes a wide spectrum of clinical illnesses, from asymptomatic colonization and mild diarrhea to pseudomembranous colitis and toxic megacolon. CDI is not adequately characterized in the pediatric age group. Our objectives were to examine: (a) trends in CDI rates, and (b) predictors of CDIs, including recurrent CDIs, in the pediatric population. Data were extracted from several validated population-based datasets from the province of Manitoba for the period from 2005 to 2015. CDI was identified from a laboratory-confirmed CDI dataset. Children aged 2–17 years with CDI were matched to children without CDI, based on age, sex, 3-digit residential postal code and duration of coverage with Manitoba Health (MH) prior to the index date (i.e., date of first CDI). Children younger than 2 years of age were excluded on the basis of apparent lack of association between carriage and disease. Rates and time trends of CDIs using previously recommended definitions were determined. Predictors of CDI sub-types were determined using multivariable logistic regression models. CDIs diagnosed in outpatient and hospital settings were identified. Cox regression analysis was used to assess for the potential predictors of CDI, including age, sex, socio-economic status (SES), co-morbidities burden, and medications used. Over the study period, 277 children with CDI were identified and matched with 1314 controls without CDI. After excluding those who had CDI before the age of 2 years old, 193 CDI from 163 children (47% males) were included. Children with and without CDI were followed for 828 and 2753 persons years respectively. There was no significant increase in CDI rates over the observation period. Children with CDI had significantly higher number of outpatient clinic visits compared to controls in the year before CDI diagnosis (p<0.0001). Co-morbid conditions that were more prevalent among children with CDI than matched controls included Hirschsprung’s disease (p<0.001) and inflammatory bowel disease (p<0.0001). More than half of CDIs were community acquired and 18.7% were healthcare facility associated. Recurrent CDIs were responsible for 10.4% of CDI episodes (range 2–6 infections). Predictors of recurrence included malignancy (Hazard ratio (HR)= 3, 95% 1.1–8.8), Type 1 diabetes (HR=4.8, 95% CI 1.1–21.4) and neurodegenerative diseases (HR=8.4, 95% CI 1.9–37.5). The incidence of CDI is not increasing among children in Manitoba. Community-acquired CDI is much more common than healthcare facility-associated. Children with malignancy, type 1 diabetes and neurodegenerative disorders are more likely to have recurrent CDI. This work was supported with an unrestricted grant from MERCK

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.256
Threshold uncertainty score0.514

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

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