Evidence map›Paper›PMID 30941028›Full record

ArticleFrontiers in neuroinformatics2019

Adopting Text Mining on Rehabilitation Therapy Repositioning for Stroke.

Guilin Meng, Yong Huang, Qi Yu, Ying Ding, David Wild, Yanxin Zhao, Xueyuan Liu, Min Song

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neuroinformatics, 2019. 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
0.7field-weighted citation impact, top 28% 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

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Review
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 at 6 institutions in 3 countries.

Guilin MengShanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Yong HuangSchool of Informatics Computing and Engineering, Indiana University, Bloomington, IN, United States.
Qi YuSchool of Management, Shanxi Medical University, Shanxi, China.
Ying DingSchool of Informatics Computing and Engineering, Indiana University, Bloomington, IN, United States.
David WildSchool of Informatics Computing and Engineering, Indiana University, Bloomington, IN, United States.
Yanxin ZhaoShanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Xueyuan LiuShanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Min SongSchool of Informatics, Yonsei University, Seoul, South Korea.
Indiana University Bloomington · USShanghai Tenth People's Hospital · CNShanxi Medical University · CNTongji University · CNWuhan University · CNYonsei University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is a common disabling disease that severely affects the daily life of patients. Accumulating evidence indicates that rehabilitation therapy can improve movement function. However, no clear guidelines have specific and effective rehabilitation therapy schemes, and the development of new rehabilitation techniques has been relatively slow. This study used a text mining approach, the ABC model, to identify an existing rehabilitation candidate therapy method that is most likely to be repositioned for stroke. In the model, we built the internal links of stroke (A), assessment scales (B), and rehabilitation therapies (C) in PubMed and the links were related to upper limb function measurements for patients with stroke. In the first step, using E-utility, we retrieved both stroke-related assessment scales and rehabilitation therapy records and then compiled two datasets, which were called Stroke_Scales and Stroke_Therapies, respectively. In the next step, we crawled all rehabilitation therapies co-occurring with the Stroke_Therapies and then named them as All_Therapies. Therapies that were already included in Stroke_Therapies were deleted from All_Therapies; therefore, the remaining therapies were the potential rehabilitation therapies, which could be repositioned for stroke after subsequent filtration by a manual check. We identified the top-ranked repositioning rehabilitation therapy and subsequently examined its clinical validation. Hand-arm bimanual intensive training (HABIT) was ranked the first in our repositioning rehabilitation therapies and had the most interaction links with Stroke_Scales. HABIT significantly improved clinical scores on assessment scales [Fugl-Meyer Assessment (FMA) and action research arm test (ARAT)] in the clinical validation study for acute stroke patients with upper limb dysfunction. Therefore, based on the ABC model and clinical validation, HABIT is a promising repositioned rehabilitation therapy for stroke, and the ABC model is an effective text mining approach for rehabilitation therapy repositioning. The findings in this study would be helpful in clinical knowledge discovery.

Indexed as

ABC modelhand-arm bimanual intensive trainingstroketext miningupper extremity

Identifiers

PMID30941028
PMCPMC6433708
OpenAlexW2922987055

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.