Evidence map›Paper›PMID 35355078›Full record

ArticleThe Journal of antimicrobial chemotherapy2022

BSAC Vanguard Series: Artificial intelligence and antibiotic stewardship.

Amy Chang, Jonathan H Chen

Abstract read
In one paragraph

Article in The Journal of antimicrobial chemotherapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Amy ChangStanford University Division of Infectious Disease & Geographic Medicine, 300 Pasteur Dr., L-134, Stanford, CA 94305, USA.
Jonathan H ChenStanford Center for Biomedical Informatics Research and Division of Hospital Medicine, Stanford University, Stanford, CA, USA.

Funding

Stanford TRANSFORM I2T ProgramR25AI147369 · NIAID · STANFORD UNIVERSITY · PI CHERTOW, GLENN MATTHEW, SINGH, UPINDER · 2019 to 2023
$1.8M
NIAID NIH HHS R25 AI147369
6 · The paper itself

Abstract

Antimicrobial stewardship is a key facet in preventing antimicrobial resistance but can be difficult to put into practice. Frontline providers are faced with the unknowns of pending culture data plus the urgency of appropriate antibiotic choice to prevent sepsis-related mortality; this often leads to broad-spectrum antibiotic prescribing. Currently available resources lack a customized approach to individual patients. Artificial intelligence (AI) focused on antimicrobial stewardship may create a unique opportunity to provide individualized, real-time recommendations to providers on appropriate, but narrower spectrum, antibiotic options. We envision that, with further advances in AI, personalized clinical decision support tools to optimize antibiotic prescribing could be available within the next decade.

Indexed as

Antimicrobial StewardshipSepsisAnti-Bacterial AgentsArtificial IntelligenceHumansAnti-Bacterial Agents

Identifiers

PMID35355078
PMCPMC10410095

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.