OBJECTIVE: To determine whether physical performance measures commonly used in clinical settings can discriminate fallers from nonfallers and predict falls in older adults with dementia.
METHODS: Systematic review and meta-analysis.
METHODS: Older adults with dementia residing in the community, hospitals, and residential care facilities.
METHODS: MEDLINE, Embase, PsycINFO, CINAHL, SPORTDiscus, the Cochrane Library, and the PEDro databases were searched from inception until December 27, 2023 (PROSPERO registration number: CRD42022303670). Retrospective or prospective studies that evaluated the associations between physical performance measures and falls in older adults with dementia were included. A random effects model was used to calculate the standardized mean difference (SMD) and 95% CI for each physical performance measure between fallers and nonfallers. Sensitivity analyses were conducted on the longitudinal studies to determine the ability of physical performance measures to predict future falls.
RESULTS: Twenty-eight studies were included in this review (n = 3542). The 5-time chair stand test [SMD = 0.23 (0.01, 0.45)], the Berg Balance Scale [SMD = -0.52 (-0.87, -0.17)], postural sway when standing on the floor [SMD = 0.25 (0.07, 0.43)] and on a foam surface [SMD = 0.45 (0.25, 0.66)], and the Short Physical Performance Battery total score [SMD = -0.46 (-0.66, -0.27)] could discriminate fallers from nonfallers. Sensitivity analyses showed that gait speed could predict future falls in longitudinal cohort studies [SMD = -0.29 (-0.49, -0.08)]. Subgroup analyses showed that gait speed [SMD = -0.21 (-0.38, -0.05)] and the Timed Up and Go test [SMD = 0.54 (0.16, 0.92)] could identify fallers staying in residential care facilities or hospitals.
CONCLUSIONS: The 5-time chair stand test, the Berg Balance Scale, postural sway when standing on the floor and a foam surface, and the Short Physical Performance Battery can be used to predict falls in older adults with dementia. Gait speed and the Timed Up and Go test can be used to predict falls in institutionalized older adults with dementia. Clinicians are recommended to use these physical performance measures to assess fall risk in older adults with dementia.

We assessed the test-retest reliability and discriminative ability of a somatosensory temporal discrimination (SSTD) assessment tool for fibromyalgia syndrome (FMS) and determined if pain-related variables were associated with SSTD performance. Twenty-five women with FMS and twenty-five asymptomatic women were assessed during two sessions 7 to 10 days apart. The proportion of correct responses (range 0-100) was calculated. Sociodemographic information was collected for both groups. The participants with FMS also completed the widespread pain index and the Brief Pain Inventory. Test-retest reliability was verified by calculating intraclass correlation coefficients. Discriminative ability was verified by a between-group comparison of scores using a t-test. Associations between SSTD score and pain variables were tested using Pearson or Spearman correlation coefficients. The test-retest reliability of the SSTD score was excellent (ICC > 0.9, CI: 0.79-0.96) for the asymptomatic group and good for the FMS group (ICC: 0.81, 95% CI: 0.62-0.91). The median (Q1-Q3) test session SSTD score differed significantly between the FMS 84.1 (71-88) and the asymptomatic 91.6 (83.4-96.1) groups (p < 0.001). Only pain duration was associated with the SSTD score. In conclusion, the new SSTD test seems reliable for people with FMS and is discriminative. Further studies should examine its sensitivity to change and correlations with other SSTD tests.

OBJECTIVE: To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer.
METHODS: Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed.
RESULTS: The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups.
CONCLUSIONS: Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

Being born small for gestational age (SGA) at term increases the risk of adverse health outcomes. We examined whether self-reported mental health differed between adults born SGA and non-SGA at term and could be used to screen for psychiatric diagnoses.
We used the Strengths and Difficulties Questionnaire to gather data from 68 participants born SGA and 88 non-SGA controls at a mean age of 26.5 years. Group differences were analysed by linear regression. We calculated the area under the curve and the sensitivity, specificity and predictive values for psychiatric diagnoses.
The mean total difficulties score was 1.9 (95% confidence interval 0.4-3.5) points higher for participants born SGA. They also reported more internalising and emotional problems (p < 0.05). The areas under the curve were 0.82 and 0.68 in the SGA and control groups, respectively. Among participants born SGA, the 90th percentile cut-off had a sensitivity of 0.38, a specificity of 0.93 and positive and negative predictive values of 0.75 and 0.71. The 80th percentile cut-off had higher sensitivity and lower specificity.
Adults born SGA reported more mental health difficulties than non-SGA controls. The low sensitivity using the 90th percentile cut-off suggests that a lower cut-off should be considered.

The Prognostic Nutritional Index (PNI) is widely recognized as a screening tool for nutrition. We retrospectively examined the impact of PNI in patients with chronic liver disease (CLD, n = 319, median age = 71 years, 153 hepatocellular carcinoma (HCC) patients) as an observational study. Factors associated with PNI < 40 were also examined. The PNI correlated well with the albumin-bilirubin (ALBI) score and ALBI grade. The 1-year cumulative overall survival rates in patients with PNI ≥ 40 (n = 225) and PNI < 40 (n = 94) were 93.2% and 65.5%, respectively (p < 0.0001). In patients with (p < 0.0001) and without (p < 0.0001) HCC, similar tendencies were found. In the multivariate analysis, hemoglobin (p = 0.00178), the presence of HCC (p = 0.0426), and ALBI score (p < 0.0001) were independent factors linked to PNI < 40. Receiver operating characteristic (ROC) curve analysis based on survival for the PNI yielded an area under the ROC curve of 0.79, with sensitivity of 0.80, specificity of 0.70, and an optimal cutoff point of 42.35. In conclusion, PNI can be a predictor of nutritional status in CLD patients. A PNI of <40 can be useful in predicting the prognosis of patients with CLD.

SARC-F is a well-accepted screening tool for sarcopenia. A SARC-F value of 1 point is reported to be more discriminating in identifying sarcopenia than 4 points (recommended cutoff point). The prognostic impact of the SARC-F score was investigated in patients with liver disease (LD, n = 269, median age = 71 years, 96 hepatocellular carcinoma (HCC) cases). Factors associated with SARC-F ≥ 4 points and SARC-F ≥ 1 point were also examined. In the multivariate analysis, age (p = 0.048), and Geriatric Nutritional Risk Index (GNRI) score (p = 0.0365) were significant factors linked to SARC-F ≥ 1 point. In our patients with LD, the SARC-F score is well correlated with the GNRI score. The 1-year cumulative overall survival ratio in patients with SARC-F ≥ 1 (n = 159) and SARC-F 0 (n = 110) was 78.3% and 90.1% (p = 0.0181). After excluding 96 HCC cases, similar tendencies were found (p = 0.0289). In the receiver operating curve (ROC) analysis based on the prognosis for the SARC-F score, the area under the ROC was 0.60. The sensitivity was 0.57, the specificity was 0.62, and the optimal cutoff point of the SARC-F score was 1. In conclusion, sarcopenia in LDs can be affected by nutritional conditions. A SARC-F score of ≥1 is more useful than a score of 4 in predicting the prognosis of patients with LD.

UNASSIGNED: Research on asthma control levels and quality of life is essential for children with asthma during their growth stage. Therefore, it is necessary to develop a questionnaire that can be used for monitoring and evaluating the disease control effectiveness and quality of life of children with asthma in China and to conduct a preliminary evaluation for its reliability, validity, and discriminative ability.
UNASSIGNED: The questionnaire was created through a literature review and qualitative interviews for a targeted population. Based on the previous work, 30 caregivers of children with asthma and 5 experienced pediatricians reviewed and discussed a collection of items. Then, 72 items were screened and selected to form the draft questionnaire. After three rounds of investigation (with 240, 503, and 360 participants, respectively), the final questionnaire was established according to the evaluation results. The structure of the questionnaire was explored through confirmatory factor analysis. Exploratory factor analysis and variability analysis were applied based on the first two rounds of investigation. Reliability, construct validity, and discriminative ability were evaluated based on the third round of investigation.
UNASSIGNED: The questionnaire contains 6 dimensions and 34 items, and the total cumulative variance contribution rate was 54.96%; Cronbach\'s α coefficient was 0.91; the split-half reliability coefficient was 0.75, and the test-retest reliability coefficient was 0.74. The children\'s age, gender, residence, asthma attack in the last three months, caregivers\' education background, and monthly income per caregiver were correlated with patient-reported outcomes of children with asthma.
UNASSIGNED: The questionnaire appeared to have good reliability, construct validity, and discriminative ability in children with asthma in China.

UNASSIGNED: Sit-To-Stand (STS) tests are reported as feasible alternatives for the assessment of functional fitness but the reliability of these tests in people with coronary artery disease (CAD) has not been reported. This study explored the test-retest reliability, convergent and known-groups validity of the five times, 30-sec and 1-min sit-to-stand test (FTSTS test, 30-s STS test and 1-min STS test respectively) in patients with CAD. The feasibility of applying these tests to distinguish the level of risk for cardiovascular events in CAD patients was also investigated.
UNASSIGNED: Patients with stable CAD performed a 6MWT and 3 STS tests in random order on the same day. Receiver operating characteristic (ROC) curve analyses were conducted using STS test data to differentiate patients with low or high risk of cardiovascular events based on the risk level determined by distance covered in the 6MWT as > or ≤ 419 m. Thirty patients repeated the 3 STS tests on the following day.
UNASSIGNED: 112 subjects with diagnoses of atherosclerosis or post-percutaneous coronary intervention, or post-acute myocardial infarction (post-AMI) participated in the validity analysis. All 3 STS tests demonstrated moderate and significant correlation with the 6MWT (coefficient values r for the FTSTS, 30-s STS and 1-min STS tests were-0.53, 0.57 and 0.55 respectively). Correlations between left ventricular ejection fraction (LVEF) and all STS tests and between 6MWT and LVEF were only weak (r values ranged from 0.27 to 0.31). Subgroup analysis showed participants in the post-AMI group performed worse in all tests compared to non-myocardial infarction (non-MI) group. The area under the curve (AUC) was 0.80 for FTSTS (sensitivity: 75.0%, specificity: 73.8%, optimal cut-off: >11.7 sec), and the AUC, sensitivity, specificity and optimal cut-off for 30-s STS and 1-min STS test were 0.83, 75.0%, 76.2%, ≤ 12 repetitions and 0.80, 71.4%, 73.8%, ≤ 23 repetitions respectively. The intraclass correlation coefficients (ICC) for repeated measurements of the FTSTS, 30-s STS and 1-min STS tests were 0.96, 0.95 and 0.96 respectively, with the minimal detectable change (MDC95) computed to be 1.1 sec 1.8 repetitions and 3.9 repetitions respectively.
UNASSIGNED: All STS tests demonstrated good test-retest reliability, convergent and known-groups validity. STS tests may discriminate low from high levels of risk for a cardiovascular event in patients with CAD.

UNASSIGNED: Delayed achievement of motor milestones may be an early indicator of motor difficulties. Parent-reported questionnaires may serve as an efficient, low-cost screening to identify infants in need of further clinical assessment, and thus be a helpful tool in busy health care centers.
UNASSIGNED: To examine the ability of the Ages and Stages Questionnaire, second edition (ASQ-2) to indicate motor difficulties in infants using the Infant Motor Profile (IMP) as the reference standard.
UNASSIGNED: A cross-sectional design was applied to examine the correlation between parent-reported data of the ASQ-2 and data from physiotherapist assessment using IMP. Included were 432 mainly low-risk infants aged 3-12 months from primary care.
UNASSIGNED: Overall, ASQ-2 gross and fine motor scores did not correlate well with the IMP total or domain scores. The ASQ-2 gross motor cut point (> 2SD below the mean), showed 34.3% sensitivity and 96.7% specificity using the 15th percentile from IMP performance domain as reference standard. The positive predictive value to indicate motor difficulties was 48%.
UNASSIGNED: The motor domains of ASQ-2 have poor ability to identify infants with motor difficulties as indicated by their IMP scores in low-risk infants.

BACKGROUND: Fall-risk assessment is complex. Based on current scientific evidence, a multifactorial approach, including the analysis of physical performance, gait parameters, and both extrinsic and intrinsic risk factors, is highly recommended. A smartphone-based app was designed to assess the individual risk of falling with a score that combines multiple fall-risk factors into one comprehensive metric using the previously listed determinants.
OBJECTIVE: This study provides a descriptive evaluation of the designed fall-risk score as well as an analysis of the app\'s discriminative ability based on real-world data.
METHODS: Anonymous data from 242 seniors was analyzed retrospectively. Data was collected between June 2018 and May 2019 using the fall-risk assessment app. First, we provided a descriptive statistical analysis of the underlying dataset. Subsequently, multiple learning models (Logistic Regression, Gaussian Naive Bayes, Gradient Boosting, Support Vector Classification, and Random Forest Regression) were trained on the dataset to obtain optimal decision boundaries. The receiver operating curve with its corresponding area under the curve (AUC) and sensitivity were the primary performance metrics utilized to assess the fall-risk score\'s ability to discriminate fallers from nonfallers. For the sake of completeness, specificity, precision, and overall accuracy were also provided for each model.
RESULTS: Out of 242 participants with a mean age of 84.6 years old (SD 6.7), 139 (57.4%) reported no previous falls (nonfaller), while 103 (42.5%) reported a previous fall (faller). The average fall risk was 29.5 points (SD 12.4). The performance metrics for the Logistic Regression Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gaussian Naive Bayes Model were AUC=0.9, sensitivity=100%, specificity=52%, and accuracy=73%. The performance metrics for the Gradient Boosting Model were AUC=0.85, sensitivity=88%, specificity=62%, and accuracy=73%. The performance metrics for the Support Vector Classification Model were AUC=0.84, sensitivity=88%, specificity=67%, and accuracy=76%. The performance metrics for the Random Forest Model were AUC=0.84, sensitivity=88%, specificity=57%, and accuracy=70%.
CONCLUSIONS: Descriptive statistics for the dataset were provided as comparison and reference values. The fall-risk score exhibited a high discriminative ability to distinguish fallers from nonfallers, irrespective of the learning model evaluated. The models had an average AUC of 0.86, an average sensitivity of 93%, and an average specificity of 58%. Average overall accuracy was 73%. Thus, the fall-risk app has the potential to support caretakers in easily conducting a valid fall-risk assessment. The fall-risk score\'s prospective accuracy will be further validated in a prospective trial.