Evidence map›Paper›PMID 29424098›Full record

Trial reportOrthopaedic surgery2018

Diagnosis of Compressed Nerve Root in Lumbar Disc Herniation Patients by Surface Electromyography.

Wei Li, Yan-Cheng Liu, Chen-Fan Zheng, Jun Miao, Hui Chen, Hai-Ying Quan, Song-Hua Yan, Kuan Zhang

Open access · bronzeAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Orthopaedic surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

12 citing papers in PubMed, 16 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

8 authors at 3 institutions in 1 country.

Wei LiSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Yan-Cheng LiuDepartment of Spinal Surgery, Tianjin Hospital, Tianjin, China.
Chen-Fan ZhengSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Jun MiaoDepartment of Spinal Surgery, Tianjin Hospital, Tianjin, China.
Hui ChenSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Hai-Ying QuanSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Song-Hua YanSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Kuan ZhangSchool of Biomedical Engineering, Capital Medical University, Beijing, China.
Capital Medical University · CNTianjin Hospital · CNChinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo establish a logistic regression model using surface electromyography (SEMG) parameters for diagnosing the compressed nerve root at L

methodsThis study recruited 24 patients with L

resultsThe accuracy of the SEMG model ranged from 85.71% to 100%, with an average of 93.57%. The sensitivity, specificity, AUC, and kappa value of the logistic regression model were 0.98 ± 0.05, 0.92 ± 0.09, 0.95 ± 0.04 (P = 0.006), and 0.87 ± 0.11, respectively (P = 0.001). The final diagnostic model was: P=1-11+ey; y = 10.76 - (5.95 × TA_RMS Ratio) - (0.38 × TA_RMS Peak Time Ratio) - (5.44 × 44 × LG_RMS Peak Time Ratio). L

conclusionsThe logistic regression model developed in this study showed high diagnostic accuracy in detecting the compressed nerve root (L

Indexed as

AdultAgedAged, 80 and overElectromyographyFemaleHumansIntervertebral Disc DegenerationIntervertebral Disc DisplacementLogistic ModelsLumbar VertebraeMagnetic Resonance ImagingMaleMiddle AgedRadiculopathyROC CurveSacrumCompressed nerve rootDiagnosisLogistic regression modelLumbar disc herniationSurface electromyography

Identifiers

PMID29424098
PMCPMC6594484
OpenAlexW2793951091

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.