Evidence map›Paper›PMID 39712700›Full record

ArticleCureus2024

Predicting Postoperative Motor Function After Brain Tumor Resection With Motor Evoked Potential Monitoring Using Decision Tree Analysis.

Takeo Yuno, Yusuke Nakade, Mitsutoshi Nakada, Masashi Kinoshita, Masako Nakata, Shiori Nakagawa, Hiroyasu Oe, Mika Mori, Takashi Wada, Hajime Kanamori

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Article in Cureus, 2024. 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
0cells of the map it votes in
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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Takeo YunoDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Yusuke NakadeDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Mitsutoshi NakadaDepartment of Neurosurgery, Kanazawa University, Kanazawa, JPN.
Masashi KinoshitaDepartment of Neurosurgery, Kanazawa University, Kanazawa, JPN.
Masako NakataDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Shiori NakagawaDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Hiroyasu OeDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Mika MoriDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Takashi WadaDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.
Hajime KanamoriDepartment of Clinical Laboratory, Kanazawa University Hospital, Kanazawa, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Motor evoked potential (MEP) monitoring is a commonly employed method in neurosurgery to prevent postoperative motor dysfunction. However, it has low prediction accuracy for postoperative paralysis. This study aimed to develop a decision tree (DT) model for predicting postoperative motor function using MEP monitoring data. Methodology In this retrospective cohort study, we used datasets, comprising 14 variables including MEP amplitudes, obtained from 125 patients who underwent brain tumor resection with intraoperative MEP monitoring at our hospital. Prediction models were developed using DT and receiver operating characteristic (ROC) curve analyses. Model performance was assessed for accuracy, sensitivity, specificity, kappa (

Indexed as

brain tumordecision treemachine learningmotor evoked potentialsparalysis

Identifiers

PMID39712700
PMCPMC11662959

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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.