Evidence map›Paper›PMID 37443532›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning.

Daisuke Oura, Soichiro Takamiya, Riku Ihara, Yoshimasa Niiya, Hiroyuki Sugimori

Abstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
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

5 authors.

Daisuke OuraDepartment of Radiology, Otaru General Hospital, Otaru 047-0152, Japan.ORCID 0000-0002-3143-7577
Soichiro TakamiyaDepartment of Neurosurgery, Otaru General Hospital, Otaru 047-0152, Japan.ORCID 0000-0001-5972-5047
Riku IharaDepartment of Radiology, Otaru General Hospital, Otaru 047-0152, Japan.
Yoshimasa NiiyaDepartment of Neurosurgery, Otaru General Hospital, Otaru 047-0152, Japan.
Hiroyuki SugimoriFaculty of Health Sciences, Hokkaido University, Sapporo 060-0812, Japan.ORCID 0000-0002-7796-5113

Funding

JSPS KAKENHI 21K07586.
6 · The paper itself

Abstract

Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data. The threshold ranged from 620 × 10

Indexed as

acute ischemic strokeADCmachine learningMRI

Identifiers

PMID37443532
PMCPMC10340725

What OpenQuestion holds

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LicenceCC BY
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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.