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Record W3097881262 · doi:10.1093/pch/pxz073

Early-onset neonatal sepsis: Organism patterns between 2009 and 2014

2019· article· en· W3097881262 on OpenAlexaff
Michael Sgro, Douglas M. Campbell, Kaitlyn Luisa Mellor, Kathleen Hollamby, Jaya Bodani, Prakesh S. Shah

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsMount Sinai HospitalRegina Qu'Appelle Health RegionUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsOrganismNeonatal sepsisSepsisGestational ageNeonatal intensive care unitEscherichia coliMedicineStreptococcusBiologyPediatricsMicrobiologyInternal medicineBacteriaPregnancyGenetics

Abstract

fetched live from OpenAlex

Abstract Objective To evaluate trends in organisms causing early-onset neonatal sepsis (EONS). Congruent with recent reports, we hypothesized there would be an increase in EONS caused by Escherichia coli. Study Design National data on infants admitted to neonatal intensive care units from 2009 to 2014 were compared to previously reported data from 2003 to 2008. We report 430 cases of EONS from 2009 to 2014. Bivariate analyses were used to analyze the distribution of causative organisms over time and differences by gestational age. Linear regression was used to estimate trends in causative organisms. Results Since 2003, there has been a trend of increasing numbers of cases caused by E coli (P<0.01). The predominant organism was E coli in preterm infants and Group B Streptococcus in term infants. Conclusions With the majority of EONS cases now caused by E coli, our findings emphasize the importance of continued surveillance of causative organism patterns and developing approaches to reduce cases caused by E coli.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.016
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.001

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 head0.261
Teacher spread0.252 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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