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Record W4386705423 · doi:10.1111/apa.16972

Short course of intravenous antibiotics in the treatment of uncomplicated proven neonatal bacterial sepsis: A systematic review

2023· review· en· W4386705423 on OpenAlexaff
Alanoud Aljarbou, Carlos A. Cuello‐García, Ana Teresa Figueiredo Stochero Leslie

Bibliographic record

VenueActa Paediatrica · 2023
Typereview
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsCochraneMcMaster UniversityImpact
Fundersnot available
KeywordsMedicineAntibioticsSepsisNeonatal sepsisIntensive care medicineIntravenous antibioticsInternal medicineMicrobiology

Abstract

fetched live from OpenAlex

AIM: To evaluate the efficacy and harms of a short (7-10 days) compared with a standard (10-14 days) duration of antibiotics in culture-proven neonatal sepsis for reducing all-cause mortality, treatment failure and duration of hospitalisation. METHODS: Medline, EMBASE and Cochrane CENTRAL were searched for randomised trials. RESULTS: We included five studies, all conducted in India (447 infants with a gestational age greater than 32 weeks). Except for one study, all studies were at high risk of bias. All-cause mortality was reported in three studies with only one death reported in the standard duration regimen arm (243 patients, very low certainty). A meta-analysis showed no evidence of the effect on treatment failure (RR of 1.47 [95% CI 0.48-4.50], 440 patients, five studies, very low certainty) of short-term antibiotics. Short-term antibiotic regimen shortened the duration of hospitalisation by 4 days (mean difference of -4.04 days [95% CI -5.47 to -2.61]; 4 studies; 371 patients; very low certainty). CONCLUSION: Among studies focused on infants born with a gestational age greater than 32 weeks, short-term administration of antibiotics may shorten the duration of hospitalisation, but the evidence is very uncertain. The evidence on other predefined outcomes is very uncertain to draw definite conclusions.

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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.279
Threshold uncertainty score0.781

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.353
Teacher spread0.301 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations5
Published2023
Admission routes1
Has abstractyes

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