Evidence map›Paper›PMID 42542500›Full record

ArticleEuropean journal of orthopaedic surgery & traumatology : orthopedie traumatologie2026

Development and internal validation of a clinical prediction model for deep surgical site infection after open extremity fractures: the BIGB

Takahiro Inui, Keisuke Ishii, Takashi Suzuki, Kentaro Matsui, Taketo Kurozumi, Koichi Inokuchi, Yoshinobu Watanabe, Hirotaka Kawano

Abstract readValidation StudyMulticenter Study
PubMed Publisher
In one paragraph

Article in European journal of orthopaedic surgery & traumatology : orthopedie traumatologie, 2026. 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

8 authors.

Takahiro InuiDepartment of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, Japan. inui.takahiro.uw@teikyo-u.ac.jp.ORCID http://orcid.org/0000-0003-4733-5281
Keisuke IshiiDepartment of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, Japan.
Takashi SuzukiTrauma and Reconstruction Center, Teikyo University Hospital, Tokyo, Japan.
Kentaro MatsuiDepartment of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, Japan.
Taketo KurozumiTrauma and Reconstruction Center, Teikyo University Hospital, Tokyo, Japan.
Koichi InokuchiTrauma Center, Saitama Medical Center, Saitama Medical University, Saitama, Japan.
Yoshinobu WatanabeDepartment of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, Japan.
Hirotaka KawanoDepartment of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly identification of patients at high risk of deep surgical site infection after open extremity fractures is crucial for timely intervention and improved outcomes. This study aimed to develop and internally validate a clinical prediction model, the BIGB

methodsA prospective cohort study was conducted at two university hospitals in Japan, including 570 consecutive patients with open extremity fractures. Data on 18 candidate predictors were collected in the early post-injury phase. The primary outcome was the occurrence of deep surgical site infection within three months of injury. A backward stepwise logistic regression model was used to build the prediction model. Model performance was assessed using the area under the curve (AUC) and internal validation with bootstrap methods.

resultsThe BIGB

conclusionThe BIGB

Indexed as

Fractures, OpenSurgical Wound InfectionAdultAgedFemaleHumansJapanMaleMiddle AgedPrediction Methods, MachineProspective StudiesRisk AssessmentRisk FactorsClinical prediction modelDeep surgical site infectionOpen fracture

Identifiers

PMID42542500

What OpenQuestion holds

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Registered trials

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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.