Evidence map›Paper›PMID 29780412›Full record

ArticleGastroenterology research and practice2018

A Prediction Model for Recognizing Strangulated Small Bowel Obstruction.

Xiaming Huang, Guan Fang, Jie Lin, Keyu Xu, Hongqi Shi, Lei Zhuang

Open access · goldAbstract read
In one paragraph

Article in Gastroenterology research and practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 8% of its field
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

21 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Xiaming HuangDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-2553-4960
Guan FangDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-9558-2802
Jie LinDepartment of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0001-6055-5903
Keyu XuDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-2566-137X
Hongqi ShiDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0002-4114-724X
Lei ZhuangDepartment of General Surgery, The First affiliated Hospital of Wenzhou Medical University, Wenzhou, China.ORCID 0000-0001-5261-2154
Wenzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionEarly and accurate diagnosis of strangulated small bowel obstruction (SSBO) is difficult. This study aimed to devise a prediction model for predicting the risk of SSBO. MATERIALS AND

methodsA database of 417 patients who had clinical symptoms of intestinal obstruction confirmed by computed tomography (CT) were evaluated for inclusion in this study. Symptoms and laboratory and radiologic findings of these patients were collected after admission. These clinical factors were analyzed using logistic regression. A logistic regression model was applied to identify determinant variables and construct a clinical score that would predict SSBO.

resultsSeventy-six patients were confirmed to have SSBO, 169 patients required surgery but had no evidence of intestinal ischemia, and 172 patients were successfully managed conservatively. In multivariate logistic regression analysis, body temperature ≥ 38.0°C, positive peritoneal irritation sign, white blood cell (WBC) count > 10.0 × 10^9/L, thick-walled small bowel ≥3 mm, and ascites were significantly associated with SSBO. A new prediction model with total scores ranging from 0 to 481 was developed with these five variables. The area under the curve (AUC) of the new prediction model was 0.935.

conclusionsOur prediction model is a good predictive model to evaluate the severity of SBO.

Identifiers

PMID29780412
PMCPMC5892273
OpenAlexW2789980118

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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.