- 著者
- Hiroyuki Hayashi Midori Iwai Hiroka Matsuoka Daiki Nakashima Shugo Nakamura Ayumi Kubo Naoki Tomiyama
- 出版者
- 理学療法科学学会
- 雑誌
- Journal of Physical Therapy Science (ISSN:09155287)
- 巻号頁・発行日
- vol.28, no.4, pp.1228-1232, 2016 (Released:2016-04-28)
- 参考文献数
- 23
- 被引用文献数
- 8
[Purpose] (1) The aim of this study was to examine relations between clinical and functional assessment and discharge destination and (2) to identify the optimal cutoff point for estimating discharge to home after inpatient rehabilitation. [Subjects] The subjects were 54 hip fracture patients (15 males, 39 females; mean age 81.3 ± 7.4 years) living alone. [Methods] The patients were classified into two groups: those discharged to home and those admitted to an institution. Age, gender, side of fracture, fracture type, number of comorbidities, Functional Independence Measure motor score, and Functional Independence Measure cognitive score were compared between groups. Multiple logistic regression analysis was conducted with discharge to home as the dependent variable and age, gender, side of fracture, fracture type, number of comorbidities, Functional Independence Measure motor score, and Functional Independence Measure cognitive score as independent variables. A receiver operating characteristic curve analysis was used to identify a cutoff point for classification of the patients into the two groups. [Results] Multiple logistic regression analysis showed that the Functional Independence Measure cognitive score was a significant variable affecting the discharge destination. The receiver operating characteristic curve analysis revealed that discharge to home was predicted accurately by a Functional Independence Measure cognitive score of 23.5. [Conclusion] Information from this study is expected to be useful for determining discharge plans and for the setting of treatment goals.
- 著者
- Ayumi Kubo Azusa Kubota Haruki Ishioka Takuhiro Hizume Masaaki Ubukata Kenji Nagatomo Takaya Satoh Mitsuyoshi Yoshida Fuminori Uematsu
- 出版者
- The Mass Spectrometry Society of Japan
- 雑誌
- Mass Spectrometry (ISSN:2187137X)
- 巻号頁・発行日
- vol.12, no.1, pp.A0120, 2023-04-13 (Released:2023-04-13)
- 参考文献数
- 15
Electron ionization (EI) mass spectrum library searching is usually performed to identify a compound in gas chromatography/mass spectrometry. However, compounds whose EI mass spectra are registered in the library are still limited compared to the popular compound databases. This means that there are compounds that cannot be identified by conventional library searching but also may result in false positives. In this report, we report on the development of a machine learning model, which was trained using chemical formulae and EI mass spectra, that can predict the EI mass spectrum from the chemical structure. It allowed us to create a predicted EI mass spectrum database with predicted EI mass spectra for 100 million compounds in PubChem. We also propose a method for improving library searching time and accuracy that includes an extensive mass spectrum library.