Evidence map›Paper›PMID 38784298›Full record

ArticleCureus2024

Actual Clinical Practice Assessment: A Rapid and Easy-to-Use Tool for Evaluating Cognitive Decline Equivalent to Dementia.

Takayuki Asano, Asako Yasuda, Setsuo Kinoshita, Jun Nomoto, Takahiro Kato, Chihiro Suzuki, Han Suzuki, Toru Kinoshita, Masahiro Shigeta, Akira Homma

Abstract read
In one paragraph

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.

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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.

Takayuki AsanoResearch and Development, Nippontect Systems Company Limited, Tokyo, JPN.
Asako YasudaResearch and Development, Nippontect Systems Company Limited, Tokyo, JPN.
Setsuo KinoshitaResearch and Development, Nippontect Systems Company Limited, Tokyo, JPN.
Jun NomotoBrain Neurosurgery, Hamato Neurosurgery Clinic, Kanagawa, JPN.
Takahiro KatoBrain Neurosurgery, Soubudai Neurosurgical Clinic, Kanagawa, JPN.
Chihiro SuzukiBrain Neurosurgery, Neuroscience Center of Suzuki Neurosurgical Clinic, Saitama, JPN.
Han SuzukiBrain Neurosurgery, Neuroscience Center of Suzuki Neurosurgical Clinic, Saitama, JPN.
Toru KinoshitaPsychiatry, Nozomi Memory Clinic, Tokyo, JPN.
Masahiro ShigetaDepartment of Psychiatry, The Jikei University School of Medicine, Tokyo, JPN.
Akira HommaPsychiatry, Otafuku Memory Clinic, Ibaraki, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Screening tests reveal the early signs of cognitive decline, enabling better self-care and preparation for the future. We developed and evaluated the accuracy of a rapid (20 s) and easy-to-use tool called ONSEI, assessing the cognitive decline equivalent to dementia in actual clinical practice by correlating clinical diagnoses with the ONSEI classification. Methods In this retrospective observational study, data were collected from individuals who visited three neurosurgical clinics in neighboring prefectures of Tokyo, Japan. ONSEI analysis was performed using a smartphone or tablet. The tool adopts a machine-learning algorithm using the speaker's age, time-orientation task score, and acoustic features of spoken responses to that task. Significant differences in accuracy, sensitivity, and specificity were evaluated by Fisher's exact test. Results The overall classification accuracy of ONSEI was 98.1% (p<0.001). The sensitivity and specificity were 97.3% (p<0.001) and 98.5% (p<0.001), respectively. The proportion of correct classifications was consistent across different age groups. Conclusion ONSEI showed high classification accuracy for dementia in cognitively normal individuals in actual clinical practice, regardless of the facility at which the tests were conducted or the age of the participants. Thus, ONSEI can be useful for dementia screening and self-care.

Indexed as

automated classificationdementiadigital cognitive assessmentmachine learningspeech features

Identifiers

PMID38784298
PMCPMC11111597

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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.