Evidence map›Paper›PMID 34917534›Full record

ArticleIranian journal of public health2021

There Is a Third Condition Aside from Survival and Death That Affects Outcome Statistics: Terminal Discharge.

Jianqiao Xu, Yongqiang Chen, Jiang Wang, Guixiu Yang, Peng Yan, Zhimei Duan, Lixin Xie, Guoxin Mo

Abstract read
In one paragraph

Article in Iranian journal of public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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.

Jianqiao XuDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Yongqiang ChenDepartment of Medical Data, Beijing Renheguangtong (RHGT) Information Co., Ltd. Beijing, China.
Jiang WangDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Guixiu YangDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Peng YanDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Zhimei DuanDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Lixin XieDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.
Guoxin MoDepartment of Pulmonary & Critical Care Medicine, Chinese People Liberation Army (PLA) General Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSome patients discharged automatically are classified as terminal discharge, while their clinical outcome is survival, disrupting the results of clinical research.

methodsThe data of this study were taken from inpatients admitted to the ICU of the First Medical Center of the People's Liberation Army General Hospital, Beijing, China from 2008-2017. We collected the data regarding medications used over the three days before discharge from the group of patients who survived and the group of patients who died, and the outcomes of all patients were recalculated by three classification algorithms (AdaBoosting, Pearson correlation coefficient, observed to expected ratio-weighted cosine similarity). Our basic assumption is that if the classification result is death but the actual in-hospital outcome is survival, the associated patient was likely terminally discharged.

resultsThe coincidence rate of the outcomes calculated by the AdaBoosting algorithm was 98.1%, the coincidence rate calculated by the Pearson correlation coefficient was 61.1%, and the coincidence rate calculated by the observed to expected ratio-weighted cosine similarity was 93.4%. When the three classification methods were combined, the accuracy reached 98.56%.

conclusionThe combination of clinical rules and classification methods has a synergistic effect on judgments of patients' discharge outcomes, greatly saving time on manual retrieval and reducing the negative influence of statistics or rules.

Indexed as

Intensive care unitsMachine learningTerminal discharge

Identifiers

PMID34917534
PMCPMC8643531

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