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Record W182703545 · doi:10.1093/pch/16.5.276

Staphylococcus aureus bloodstream infections in children: A population-based assessment

2011· article· en· W182703545 on OpenAlexaffabout
Otto G Vanderkoo, Daniel B. Gregson, James D. Kellner, Kevin B. Laupland

Bibliographic record

VenuePaediatrics & Child Health · 2011
Typearticle
Languageen
FieldMedicine
TopicAntimicrobial Resistance in Staphylococcus
Canadian institutionsUniversity of CalgaryAlberta Health ServicesCalgary Laboratory Services
Fundersnot available
KeywordsStaphylococcus aureusBloodstream infectionMedicinePopulationStaphylococcal infectionsMicrobiologyIntensive care medicineBiologyEnvironmental healthBacteria

Abstract

fetched live from OpenAlex

BACKGROUND: Although Staphylococcus aureus is a major cause of bloodstream infections, population-based data on these infections in children are limited. OBJECTIVE: To describe the epidemiology of S aureus bacteremia in children. METHODS: Population-based surveillance for all incident S aureus bacteremias was conducted among children (18 years of age or younger) living in the Calgary Health Region (Alberta) from 2000 to 2006. RESULTS: During the seven-year study, 120 S aureus bloodstream infections occurred among 119 patients; 27% were nosocomial, 18% health care associated and 56% community acquired. The annual incidence was 6.5/100,000 population and 0.094/1000 live births. A total of 52% had a significant underlying condition, and this was higher for nosocomial cases. Bone and joint (40%), bacteremia without a focus (33%), and skin and soft tissue infections (15%) were the most common clinical syndromes. Infections due to methicillin-resistant S aureus were uncommon (occurring in one infection) and three patients (2.5%) died. CONCLUSIONS: S aureus bacteremia is an important cause of morbidity in the paediatric age group. Underlying medical conditions and implanted devices are important risk factors. Methicillin-resistant S aureus and mortality rates are low.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.015
GPT teacher head0.293
Teacher spread0.277 · 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.

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

Citations43
Published2011
Admission routes2
Has abstractyes

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