Evidence map›Paper›PMID 38234303›Full record

ArticleFrontiers in bioengineering and biotechnology2023

Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data.

Takayuki Ogasawara, Masahiko Mukaino, Kenichi Matsunaga, Yoshitaka Wada, Takuya Suzuki, Yasushi Aoshima, Shotaro Furuzawa, Yuji Kono, Eiichi Saitoh, Masumi Yamaguchi and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
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

12 authors at 4 institutions in 1 country.

Takayuki OgasawaraNTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan.
Masahiko MukainoDepartment of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan.
Kenichi MatsunagaNTT Device Innovation Center, NTT Corporation, Atsugi, Japan.
Yoshitaka WadaDepartment of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan.
Takuya SuzukiDepartment of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan.
Yasushi AoshimaDepartment of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan.
Shotaro FuruzawaDepartment of Rehabilitation Medicine, Fujita Health University Hospital, Toyoake, Japan.
Yuji KonoDepartment of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan.
Eiichi SaitohDepartment of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan.
Masumi YamaguchiNTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan.
Yohei OtakaDepartment of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Toyoake, Japan.
Shingo TsukadaNTT Basic Research Laboratories and Bio-Medical Informatics Research Center, NTT Corporation, Atsugi, Japan.
Fujita Health University · JPFujita Health University Hospital · JPNTT Basic Research Laboratories · JPNTT (Japan) · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

bedroom-stay durationlocation trackingmachine learningrehabilitationstrokewearable sensors

Identifiers

PMID38234303
PMCPMC10791943
OpenAlexW4390571270

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.