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.

Syndromes are defined with signs or symptoms that occur together and represent conditions. We use a data-driven approach to identify the deadliest and most death-averse frailty syndromes based on frailty symptoms. A list of 72 frailty symptoms was retrieved based on three frailty indices. We used data from the Health and Retirement Study (HRS), a longitudinal study following Americans aged 50 years and over. Principal component (PC)-based syndromes were derived based on a principal component analysis of the symptoms. Equal-weight 4-item syndromes were the sum of any four symptoms. Discrete-time survival analysis was conducted to compare the predictive power of derived syndromes on mortality. Deadly syndromes were those that significantly predicted mortality with positive regression coefficients and death-averse ones with negative coefficients. There were 2,797 of 5,041 PCbased and 964,774 of 971,635 equal-weight 4-item syndromes significantly associated with mortality. The input symptoms with the largest regression coefficients could be summed with three other input variables with small regression coefficients to constitute the leading deadliest and the most deathaverse 4-item equal-weight syndromes. In addition to chance alone, input symptoms' variances and the regression coefficients or p values regarding mortality prediction are associated with the identification of significant syndromes.Conceptually, frailty syndromes are similar to composite measures or indices that are sums of multiple input variables with equal or unequal weights 20 . Given how differently they are measured and the distinctive theories that inspired them, it is surprising that most frailty syndromes are significantly associated with major health outcomes, especially mortality. One reason is that significant health outcomes may be more likely to be published 21 . Alternatively, there are numerous candidate syndromes to screen, test, and publish. Recent findings in index mining suggest that syndromes can be searched systematically using large data sets and pre-specified rules 20 . For example, there are 72 frailty symptoms identified to form frailty syndromes in the Health and Retirement Study (HRS), and a large number of possible combinations are available 20,22 . Facing this large number of candidate syndromes, there are no well-established criteria to select clinically meaningful syndromes regardless of its statistical significance 18 . The underlying causes associated with new statistically significant frailty syndromes with important outcomes have not been identified. It is necessary to identify the factors contributing to statistical significances of frailty syndromes before assessing the importance of statistical significances in frailty syndromes. Then, a set of criteria for the selection of clinical meaningful syndromes could be developed. This study aims to identify the factors related to the statistical significances of newly generated syndromes, taking frailty syndromes as an example. The characteristics of the newly identified f...

Yen-po cheng 4 , Yi-chun Lai 5 & Wei-chih chen 6* composite diagnostic criteria are common in frailty research. We worry distinct populations may be linked to each other due to complicated criteria. We aim to investigate whether distinct populations might be considered similar based on frailty diagnostic criteria. the functional Domains Model for frailty diagnosis included four domains: physical, nutritive, cognitive and sensory functioning. Health and Retirement Study participants with two or more deficiencies in the domains were diagnosed frail. the survival distributions were analyzed using discrete-time survival analysis. the distributions of the demographic characteristics and survival across the groups diagnosed with frailty were significantly different (p < 0.05). A deficiency in cognitive functioning was associated with the worst survival pattern compared with a deficiency in the other domains (adjusted p < 0.05). The associations of the domains with mortality were cumulative without interactions. Cognitive functioning had the largest effect size for mortality prediction (Odds ratios, OR = 2.37), larger than that of frailty status (OR = 1.92). The frailty diagnostic criteria may take distinct populations as equal and potentially assign irrelevant interventions to individuals without corresponding conditions. We think it necessary to review the adequacy of composite diagnostic criteria in frailty diagnosis.

Background: Biomonitoring can be conducted by assessing the levels of chemicals in human bodies and their surroundings, for example, as was done in the Canadian Health Measures Survey (CHMS). This study aims to report the leading increasing or decreasing biomarker trends and determine their significance. Methods: We implemented a trend analysis for all variables from CHMS biomonitoring data cycles 1-5 conducted between 2007 and 2017. The associations between time and obesity were determined with linear regressions using the CHMS cycles and body mass index (BMI) as predictors. Results: There were 997 unique biomarkers identified and 86 biomarkers with significant trends across cycles. Nine of the 10 leading biomarkers with the largest decreases were environmental chemicals. The levels of 1,2,3-trimethyl benzene, dodecane, palmitoleic acid, and o-xylene decreased by more than 60%. All of the 10 chemicals with the largest increases were environmental chemicals, and the levels of 1,2,4-trimethylbenzene, nonanal, and 4-methyl-2-pentanone increased by more than 200%. None of the 20 biomarkers with the largest increases or decreases between cycles were associated with BMI. Conclusions: The CHMS provides the opportunity for researchers to determine associations between biomarkers and time or BMI. However, the unknown causes of trends with large magnitudes of increase or decrease and their unclear impact on Canadians' health present challenges. We recommend that the CHMS plan future cycles on leading trends and measure chemicals with both human and environmental samples.

Background Laparoscopic greater curvature plication (LGCP) is a new bariatric procedure that is similar to laparoscopic sleeve gastrectomy (LSG) in that it uses a restrictive mechanism. Comparative studies between LGCP and LSG were still limited. The aim of this study was to compare the clinical outcomes of the two procedures based on the same clinical conditions. Methods From January 2012 to December 2015, 260 patients with morbid obesity underwent LGCP and LSG in a single center. Data on patient demography, operation time, complications, hospital stay, body mass index loss, percentage of excess weight loss (%EWL), and improvement in comorbidities were collected. A propensity-matched analysis, incorporating pre-operative variables, was used to compare the short-term outcomes between LGCP and LSG. Results Propensity matching produced 48 patients in each group. Patients who underwent LGCP were predominately female (75.5%, 41.1% of the LSG patients were female, p = 0.028). Baseline BMI and excess weight were significantly lower in the LGCP group (p < 0.001). The LSG group showed a greater decrease in excess body weight than the LGCP group (