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Record W2331910437 · doi:10.1097/inf.0b013e31826eb4f9

Maternal Antibiotic Exposure and Risk of Antibiotic Resistance in Neonatal Early-onset Sepsis

2012· article· en· W2331910437 on OpenAlexaff
Alissa Wright, Sharon Unger, Brenda L. Coleman, Po-Po Lam, Allison McGeer

Bibliographic record

VenueThe Pediatric Infectious Disease Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsUniversity of TorontoUniversity of British Columbia
Fundersnot available
KeywordsAntibioticsMedicineAntibiotic resistanceNeonatal sepsisSepsisIntensive care medicineInternal medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

In a case-cohort study of early-onset sepsis, antibiotic resistance was more likely for infections in neonates born to mothers who were given antibiotics during pregnancy (odds ratio 4.6; 95% confidence interval: 1.1-19;P = 0.05). Risk of resistance increased with duration of antibiotics and number of antibiotic courses during pregnancy. Preterm birth and hospitalization during pregnancy were also associated with resistance. These risk factors should be considered when selecting empiric antibiotics for therapy of early-onset sepsis in infants.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.006
GPT teacher head0.236
Teacher spread0.229 · 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

Citations15
Published2012
Admission routes1
Has abstractyes

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