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Record W4311185133 · doi:10.2471/blt.22.288623

Antibiotics needed to treat multidrug-resistant infections in neonates

2022· review· en· W4311185133 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBulletin of the World Health Organization · 2022
Typereview
Languageen
FieldMedicine
TopicNeonatal and Maternal Infections
Canadian institutionsInstitute of Infection and Immunity
FundersU.S. Food and Drug Administration
KeywordsAntibioticsMedicineNeonatal sepsisAntibiotic resistancePopulationPediatricsSepsisIntensive care medicineInternal medicineMicrobiologyBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Infections remain a leading cause of death in neonates. The sparse antibiotic development pipeline and challenges in conducting neonatal research have resulted in few effective antibiotics being adequately studied to treat multidrug-resistant (MDR) infections in neonates, despite the increasing global mortality burden caused by antimicrobial resistance. Of 40 antibiotics approved for use in adults since 2000, only four have included dosing information for neonates in their labelling. Currently, 43 adult antibiotic clinical trials are recruiting patients, compared with only six trials recruiting neonates. We review the World Health Organization (WHO) priority pathogens list relevant to neonatal sepsis and propose a WHO multiexpert stakeholder meeting to promote the development of a neonatal priority antibiotic development list. The goal is to develop international, interdisciplinary consensus for an accelerated neonatal antibiotic development programme. This programme would enable focused research on identified priority antibiotics for neonates to reduce the excess morbidity and mortality caused by MDR infections in this vulnerable population.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.759
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.032
GPT teacher head0.336
Teacher spread0.305 · 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