Evidence map›Paper›PMID 40160123›Full record

ArticleSurgical infections2025

Development and Evaluation of Machine Learning Models for the Identification of Surgical Site Infection in Electronic Health Records.

Arjun Chakraborty, Kevin Lybarger, Jorge A Olivas Estebane, Judy Y Chen, Mahul Patel, Vikas O'Reilly-Shah, Peter Tarczy-Hornoch, Meliha Yetisgen, Dustin R Long

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Surgical infections, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Arjun ChakrabortyDepartment of Biomedical Informatics and Medical Information, University of Washington, Seattle, Washington, USA.
Kevin LybargerDepartment of Biomedical Informatics and Medical Information, University of Washington, Seattle, Washington, USA.
Jorge A Olivas EstebaneInfection Prevention and Control Program, Harborview Medical Center, Seattle, Washington, USA.
Judy Y ChenDepartment of Surgery, University of Washington School of Medicine, Seattle, Washington, USA.
Mahul PatelDepartment of Surgery, University of Washington School of Medicine, Seattle, Washington, USA.
Vikas O'Reilly-ShahDepartment of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, Washington, USA.
Peter Tarczy-HornochDepartment of Biomedical Informatics and Medical Information, University of Washington, Seattle, Washington, USA.
Meliha YetisgenDepartment of Biomedical Informatics and Medical Information, University of Washington, Seattle, Washington, USA.
Dustin R LongDivision of Critical Care Medicine, Department of Anesthesiology and Pain Medicine, University of Washington School of Medicine, Seattle, Washington, USA.

Funding

University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
Targeting the Patient Microbiome for the Prevention of Surgical Site Infection in Spine SurgeryK23AR080209 · NIAMS · UNIVERSITY OF WASHINGTON · PI Dustin R Long · 2022 to 2026
$833k
NIAMS NIH HHS K23 AR080209NLM NIH HHS T15 LM007442
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Electronic Health RecordsMachine LearningSurgical Wound InfectionFemaleHumansMaleNatural Language ProcessingNeural Networks, ComputerRetrospective StudiesUnited Stateselectronic health recordsmachine learningnatural language processingsurgical site infectionsurveillance

Identifiers

PMID40160123
PMCPMC13090829

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.