Evidence map›Paper›PMID 34350718›Full record

ArticleInternational wound journal2022

Risk prediction model for deep surgical site infection (DSSI) following open reduction and internal fixation of displaced intra-articular calcaneal fracture.

Kaosheng Lu, Tianxiao Ma, Chunyan Yang, Qiaoge Qu, Haibo Liu

Abstract read
In one paragraph

Article in International wound journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Kaosheng LuDepartment of Orthopaedic Surgery, The General Hospital of Jizhong Energy Xingtai Mining Group, Xingtai, PR China.
Tianxiao MaDepartment of Orthopaedic Surgery, The General Hospital of Jizhong Energy Xingtai Mining Group, Xingtai, PR China.
Chunyan YangDepartment of Orthopaedic Surgery, The General Hospital of Jizhong Energy Xingtai Mining Group, Xingtai, PR China.
Qiaoge QuDepartment of Orthopaedic Surgery, The General Hospital of Jizhong Energy Xingtai Mining Group, Xingtai, PR China.
Haibo LiuDepartment of Orthopaedic Surgery, The General Hospital of Jizhong Energy Xingtai Mining Group, Xingtai, PR China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep surgical site infection (DSSI) is a serious complication affecting the surgical outcome of displaced intra-articular calcaneal fracture, and a risk prediction model based on the identifiable risk factors will provide great clinical value in prevention and prompt interventions. This study retrospectively identified patients operated for calcaneal fracture between January 2014 and December 2019, with a follow-up ≥1 year. The data were extracted from electronic medical records, with regard to demographics, comorbidities, injury, surgery and laboratory biomarkers at admission. Univariate and multivariate logistics regression analyses were used to identify the independent factors for DSSI, thereby the risk prediction model was developed. Among 900 patients included, 2.7% developed a DSSI. The multivariate analyses identified five factors independently associated with DSSI, including current smoking (OR, 2.8; 95% confidence interval [CI], 1.3-6.4; P = .021), BMI ≥ 26.4 kg/m

Indexed as

CalcaneusFractures, BoneFracture Fixation, InternalHumansOpen Fracture ReductionRetrospective StudiesSurgical Wound InfectionTreatment Outcomecalcaneal fracturedeep surgical site infectionmultivariate analysisreceiver operating characteristic (ROC)risk prediction model

Identifiers

PMID34350718
PMCPMC8874094

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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.