Evidence map›Paper›PMID 27602370›Full record

ArticleBurns & trauma2014

Prediction of sepsis in trauma patients.

He Jin, Zheng Liu, Ya Xiao, Xia Fan, Jun Yan, Huaping Liang

Abstract read
In one paragraph

Article in Burns & trauma, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

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

  1. Pooled it
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  10. Journal of surgery and research · 2022
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  16. Obese trauma patients have increased need for dialysis.European journal of trauma and emergency surgery : official publication of the European Trauma Society · 2020
    Article
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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.

He JinState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.
Zheng LiuState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.
Ya XiaoState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.
Xia FanState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.
Jun YanState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.
Huaping LiangState Key Laboratory of Trauma, Burns and Combined Injury, Research Institute of Surgery, Daping Hospital, The Third Military Medical University, Chongqing, 400042 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Trauma is one of the leading causes of death worldwide. Approximately 39.5% of deaths occur in the hospital, and the mortality rate of delayed death caused by septic complications is still high. Early prediction of the development of sepsis can help promote early intervention and treatment for patients and contribute to improving patient outcomes. Thus so far, biomarkers, patient demographics and injury characteristics are the main methods used for predicting sepsis in trauma patients. However, studies that verify their predictive value are limited, and the results are still controversial. More work should be conducted to explore more efficient and accurate ways to predict post-traumatic sepsis.

Indexed as

infectionpredictionSepsistrauma

Identifiers

PMID27602370
PMCPMC5012019

What OpenQuestion holds

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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.