Evidence map›Paper›PMID 40852422›Full record

ArticleSage open aging

A Pattern Combining the Cognitive and Physical Risks Predicts Frailty Reversal in Community-Dwelling Older Individuals 3 years Later: A Decision Tree Analysis.

Noriki Yamaya, Tomomi Furukawa, Kazuki Kitazawa, Kazuki Hirao, Takaaki Fujita, Fusae Tozato, Yayoi Kitamura, Naoyuki Oi, Tsutomu Iwaya, Kenji Tsuchiya

Abstract read
In one paragraph

Article in Sage open aging. 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
–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

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.

Noriki YamayaNagano University of Health and Medicine, Japan.ORCID https://orcid.org/0000-0002-3951-8806
Tomomi FurukawaNagano University of Health and Medicine, Japan.
Kazuki KitazawaNagano University of Health and Medicine, Japan.
Kazuki HiraoKagoshima University, Japan.
Takaaki FujitaFukushima Medical University, Japan.
Fusae TozatoSendai Seiyo Gakuin College, Japan.
Yayoi KitamuraNagano University of Health and Medicine, Japan.
Naoyuki OiNagano University of Health and Medicine, Japan.
Tsutomu IwayaNagano University of Health and Medicine, Japan.
Kenji TsuchiyaNagano University of Health and Medicine, Japan.ORCID https://orcid.org/0000-0002-0524-9070

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify predictive risk patterns for older individuals who are likely or unlikely to reverse from frailty 3 years later using the Kihon Checklist (KCL) and a machine learning model. Data were collected from community-dwelling individuals aged 65 and over with frailty but without long-term care at baseline. A decision tree analysis revealed that the cognitive function domain of the KCL was the primary determinant of frailty reversal. Among those with low cognitive function, low physical activity predicted remaining frailty status 3 years later. The derived model achieved a specificity of 81.0% and a precision of 82.6% in predicting individuals with remaining persistent frailty. These findings suggest that the pattern combining cognitive and physical risk factors plays a key role in predicting frailty outcomes, and that the proposed model may be useful for screening individuals who require targeted interventions.

Indexed as

classificationfrail elderlyKihon checklistmachine-learningpredictive models

Identifiers

PMID40852422
PMCPMC12368317

What OpenQuestion holds

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Registered trials

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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.