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A systematic review of the relationship between body composition including muscle, fat, bone, and body water and frailty in Asian residents
Get PDFInternational guidelines suggested that overweight and underweight are risk factors for frailty. However, body composition, which directly affects body weight, was not mentioned as a risk factor. We aimed to investigate whether the body composition, including muscle, fat, bone, and body water, is a risk factor for frailty. MEDLINE, Cumulative Index to Nursing and Allied Health Literature, and Scopus were searched up to June 03, 2022. We included cohort studies or observational studies using a cross-sectional design that reported an association between body composition and frailty. Two reviewers assessed the quality of the included cohort studies. Furthermore, we examined whether body composition as a risk factor for frailty varies depending on the participant’s place of residence. Of the 3871 retrieved studies, 77 were ultimately included, 7 of which were cohort studies. The risk-of-bias evaluation in each cohort study showed that all studies had at least one concern. Low lean mass, waist circumference-defined abdominal obesity, and bone mineral density were significantly associated with frailty in the cohort studies. The results of bone mineral density were conflicted in the cross-sectional studies. Considering the participants’ place of residence, a significant association between lower-extremity muscle mass and frailty was demonstrated, particularly among Asian residents. Low lean mass and abdominal obesity were likely risk factors for frailty. These results could be useful for developing frailty prevention strategies and could have a positive impact on individual health management. Further, future studies are needed because body composition affecting frailty may differ by race.ファイル差し替え（2025/4/10）departmental bulletin pape
否定スコープ変換のソースコード
No full text下記論文で提案している，否定スコープ変換手法を実装したソースコード
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Asahi Yoshida, Yoshihide Kato, and Shigeki Matsubara. 2024. Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12359–12369, Torino, Italia. ELRA and ICCL.The source code for negation scope conversion that is proposed in the paper mentioned below.
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Asahi Yoshida, Yoshihide Kato, and Shigeki Matsubara. 2024. Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 12359–12369, Torino, Italia. ELRA and ICCL.Related paper Information:
@inproceedings{yoshida-etal-2024-negation,
title = "Negation Scope Conversion: Towards a Unified Negation-Annotated Dataset",
author = "Yoshida, Asahi and
Kato, Yoshihide and
Matsubara, Shigeki",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1057/",
pages = "12093--12099",
abstract = "Negation scope resolution is the task that identifies the part of a sentence affected by the negation cue. The three major corpora used for this task, the BioScope corpus, the SFU review corpus and the Sherlock dataset, have different annotation schemes for negation scope. Due to the different annotations, the negation scope resolution models based on pre-trained language models (PLMs) perform worse when fine-tuned on the simply combined dataset consisting of the three corpora. To address this issue, we propose a method for automatically converting the scopes of BioScope and SFU to those of Sherlock and merge them into a unified dataset. To verify the effectiveness of the proposed method, we conducted experiments using the unified dataset for fine-tuning PLM-based models. The experimental results demonstrate that the performances of the models increase when fine-tuned on the unified dataset unlike the simply combined one. In the token-level metric, the model fine-tuned on the unified dataset archived the state-of-the-art performance on the Sherlock dataset."
}datase
