Evidence map›Paper›PMID 34650753›Full record

ArticleAmerican journal of translational research2021

Prevention of deep venous thrombosis in patients undergoing knee arthroplasty by intermittent pneumatic compression apparatus.

Shengying Wang, Haiying Lu, Shuangling Li

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 30% 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

3 citing papers in PubMed, 5 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

3 authors at 1 institution in 1 country.

Shengying WangDepartment of Sports Medicine and Joint Surgery, Qingdao Municipal Hospital Qingdao 266011, Shandong, China.
Haiying LuEndoscopy Center, Qingdao Municipal Hospital Qingdao 266011, Shandong, China.
Shuangling LiDepartment of Sports Medicine and Joint Surgery, Qingdao Municipal Hospital Qingdao 266011, Shandong, China.
Qingdao University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study discussed and analyzed the preventive value of intermittent pneumatic compression combined with early rehabilitation training for deep vein thrombosis (DVT) in patients undergoing total knee arthroplasty (TKA).

methodsDuring January 2019 to April 2020, 85 patients who underwent TKA in our hospital were selected as subjects, and were randomly divided into an observation group (n=44) and control group (n=41) by table of random numbers. The control group patients received conventional nursing care after TKA surgery, while the observation group received combinative treatment of intermittent pneumatic compression therapy and early rehabilitation training like the conventional treatment in the control group. Subsequently, the circumference and mobility of knee joint, hemorrheologic indexes and the incidence of DVT between the two groups of patients before and after surgery were compared.

resultsThe knee circumferences of the two groups on 3 d and 7 d preoperatively were higher than 1 d before surgery (

conclusionCombinative treatment of intermittent pneumatic compression and early rehabilitation training can effectively improve the postoperative knee function of patients undergoing TKA, promote recovery, and effectively prevent DVT. In conclusion, the combinative treatment is worthy of clinical application.

Indexed as

deep vein thrombosisearly rehabilitation trainingIntermittent pneumatic compressiontotal knee replacement

Identifiers

PMID34650753
PMCPMC8507013
OpenAlexW3206040948

What OpenQuestion holds

Textmetadata
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.