IntroductionFrailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality.MethodsThree FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality.ResultsWe found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs.ConclusionFIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

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A phase transition between topologically distinct insulating phases involves closing and reopening the bandgap. Near the topological phase transition, the bulk energy spectrum is characterized by a massive Dirac dispersion, where the bandgap plays the role of mass. We report measurements of strain dependence of electrical transport properties of ZrTe5, which is known to host massive Dirac fermions in the bulk due to its proximity to a topological phase transition. We observe that the resistivity exhibits a pronounced minimum at a critical strain. We further find that the positive longitudinal magnetoconductance becomes maximal at the critical strain. This nonmonotonic strain dependence is consistent with the switching of sign of the Dirac mass and, hence, a strain-tuned topological phase transition in ZrTe5.

Background: Low back pain is a common health problem among hospital nurses. However, the prevalence, characteristics, and work-related risk factors of low back pain have not been widely investigated in Taiwan. Materials and Methods: This study used a cross-sectional survey of 217 hospital nurses to gather self-reported information on the prevalence of back pain, demographic and pain characteristics, and work-related risk factors from 178 respondents who indicated a past history of back pain. The association between the characteristics of back pain and work-related risk factors was also examined. Results: The lifetime prevalence of back pain was 82.03%, and the point prevalence of back pain was 43.78%. The mean pain score is 41.67. The number of years at work was significantly associated with the pain score for an individual's most recent episode of back pain, the extent of bothersomeness of back pain and leg pain, and the extent to which back pain interfered with normal work. Conclusion: Back pain is common among hospital nurses in Taiwan. Years at work are significantly associated with pain severity and disability caused by back pain.

Osteosynthesis with cannulated screws fixation is a simple, safe, economical, and reasonably effective procedure for the treatment of undisplaced femoral neck fractures in patients older than 80 years.

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ObjectivesComposite diagnostic criteria alone are likely to create and introduce biases into diagnoses that subsequently have poor relationships with input symptoms. This study aims to understand the relationships between the diagnoses and the input symptoms, as well as the magnitudes of biases created by diagnostic criteria and introduced into the diagnoses of mental illnesses with large disease burdens (major depressive episodes, dysthymic disorder, and manic episodes).SettingsGeneral psychiatric care.ParticipantsWithout real-world data available to the public, 100 000 subjects were simulated and the input symptoms were assigned based on the assumed prevalence rates (0.05, 0.1, 0.3, 0.5 and 0.7) and correlations between symptoms (0, 0.1, 0.4, 0.7 and 0.9). The input symptoms were extracted from the diagnostic criteria. The diagnostic criteria were transformed into mathematical equations to demonstrate the sources of biases and convert the input symptoms into diagnoses.Primary and secondary outcomesThe relationships between the input symptoms and diagnoses were interpreted using forward stepwise linear regressions. Biases due to data censoring or categorisation introduced into the intermediate variables, and the three diagnoses were measured.ResultsThe prevalence rates of the diagnoses were lower than those of the input symptoms and proportional to the assumed prevalence rates and the correlations between the input symptoms. Certain input or bias variables consistently explained the diagnoses better than the others. Except for 0 correlations and 0.7 prevalence rates of the input symptoms for the diagnosis of dysthymic disorder, the input symptoms could not fully explain the diagnoses.ConclusionsThere are biases created due to composite diagnostic criteria and introduced into the diagnoses. The design of the diagnostic criteria determines the prevalence of the diagnoses and the relationships between the input symptoms, the diagnoses, and the biases. The importance of the input symptoms has been distorted largely by the diagnostic criteria.

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