A typical empirical study involves choosing a sample, a research design, and an analysis path. Variation in such choices across studies leads to heterogeneity in results that introduce an additional layer of uncertainty, limiting the generalizability of published scientific findings. We provide a framework for studying heterogeneity in the social sciences and divide heterogeneity into population, design, and analytical heterogeneity. Our framework suggests that after accounting for heterogeneity, the probability that the tested hypothesis is true for the average population, design, and analysis path can be much lower than implied by nominal error rates of statistically significant individual studies. We estimate each type\'s heterogeneity from 70 multilab replication studies, 11 prospective meta-analyses of studies employing different experimental designs, and 5 multianalyst studies. In our data, population heterogeneity tends to be relatively small, whereas design and analytical heterogeneity are large. Our results should, however, be interpreted cautiously due to the limited number of studies and the large uncertainty in the heterogeneity estimates. We discuss several ways to parse and account for heterogeneity in the context of different methodologies.

UNASSIGNED: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC.
UNASSIGNED: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model.
UNASSIGNED: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work.
UNASSIGNED: This study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

UNASSIGNED: Measuring socioeconomic status (SES) as an independent variable is challenging, especially in epidemiological and social studies. This issue is more critical in large-scale studies on the national level. The present study aimed to extensively evaluate the validity and reliability of the Iranian SES questionnaire.
UNASSIGNED: This psychometric, cross-sectional study was conducted on 3000 households, selected via random cluster sampling from various areas in East Azerbaijan province and Tehran, Iran. Moreover, 250 students from Tabriz University of Medical Sciences were selected as interviewers to collect data from 40 districts in Iran. The construct validity and internal consistency of the SES questionnaire were assessed using exploratory and confirmatory factor analyses and the Cronbach\'s alpha. Data analysis was performed in SPSS and AMOS.
UNASSIGNED: The complete Iranian version of the SES questionnaire consists of 5 factors. The Cronbach\'s alpha was calculated to be 0.79, 0.94, 0.66, 0.69, and 0.48 for the occupation, self-evaluation of economic capacity, house and furniture, wealth, and health expenditure, respectively. In addition, the confirmatory factor analysis results indicated the data\'s compatibility with the 5-factor model (comparative fit index = 0.96; goodness of fit index = 0.95; incremental fit index = 0.96; root mean square error of approximation = 0.05).
UNASSIGNED: According to the results, the confirmed validity and reliability of the tool indicated that the Iranian version of the SES questionnaire could be utilized with the same structure on an extensive level and could be applicable for measuring the SES in a broader range of populations.

暂无摘要。

UNASSIGNED: To summarize recent literature on selection bias in disparities research addressing either descriptive or causal questions, with examples from dementia research.
UNASSIGNED: Defining a clear estimand, including the target population, is essential to assess whether generalizability bias or collider-stratification bias are threats to inferences. Selection bias in disparities research can result from sampling strategies, differential inclusion pipelines, loss to follow-up, and competing events. If competing events occur, several potentially relevant estimands can be estimated under different assumptions, with different interpretations. The apparent magnitude of a disparity can differ substantially based on the chosen estimand. Both randomized and observational studies may misrepresent health disparities or heterogeneity in treatment effects if they are not based on a known sampling scheme.
UNASSIGNED: Researchers have recently made substantial progress in conceptualization and methods related to selection bias. This progress will improve the relevance of both descriptive and causal health disparities research.

UNASSIGNED: To assess the external validity of randomized controlled trials (RCTs) of bariatric surgical treatment on diabetes control.
UNASSIGNED: Multisite RCTs provide the strongest evidence supporting clinical treatments and have the greatest internal validity. However, characteristics of trial participants may not be representative of patients receiving treatment in the real world. There is a need to assess how the results of RCTs generalize to all contemporary patient populations undergoing treatments.
UNASSIGNED: All patients undergoing sleeve gastrectomy at University of California Los Angeles (UCLA) between January 8, 2018 and May 19, 2023 had their baseline characteristics, weight change, and diabetes control compared with those enrolled in the surgical treatment and medications potentially eradicate diabetes efficiently (STAMPEDE) and diabetes surgery study (DSS) RCTs of bariatric surgery\'s effect on diabetes control. Weight loss and diabetes control were compared between UCLA patients who did and did not fit the entry criteria for these RCTs.
UNASSIGNED: Only 65 (17%) of 387 patients with diabetes fulfilled the eligibility criteria for STAMPEDE, and 29 (7.5%) fulfilled the criteria for DSS due to being older, having higher body mass index, and lower HbA1c. UCLA patients experienced slightly less weight loss than patients in the RCTs but had similar diabetes control. The 313 (81%) patients not eligible for study entry into either RCT had similar long-term diabetes control as those who were eligible for the RCTs.
UNASSIGNED: Even though only a very small proportion of patients undergoing bariatric surgery met the eligibility criteria for the 2 major RCTs, most patients in this contemporary cohort had similar outcomes. Diabetes outcomes from STAMPEDE and DSS generalize to most patients undergoing bariatric surgery for diabetes control.

Bulk wave acoustic time-of-flight (ToF) measurements in pipes and closed containers can be hindered by guided waves with similar arrival times propagating in the container wall, especially when a low excitation frequency is used to mitigate sound attenuation from the material. Convolutional neural networks (CNNs) have emerged as a new paradigm for obtaining accurate ToF in non-destructive evaluation (NDE) and have been demonstrated for such complicated conditions. However, the generalizability of ToF-CNNs has not been investigated. In this work, we analyze the generalizability of the ToF-CNN for broader applications, given limited training data. We first investigate the CNN performance with respect to training dataset size and different training data and test data parameters (container dimensions and material properties). Furthermore, we perform a series of tests to understand the distribution of data parameters that need to be incorporated in training for enhanced model generalizability. This is investigated by training the model on a set of small- and large-container datasets regardless of the test data. We observe that the quantity of data partitioned for training must be of a good representation of the entire sets and sufficient to span through the input space. The result of the network also shows that the learning model with the training data on small containers delivers a sufficiently stable result on different feature interactions compared to the learning model with the training data on large containers. To check the robustness of the model, we tested the trained model to predict the ToF of different sound speed mediums, which shows excellent accuracy. Furthermore, to mimic real experimental scenarios, data are augmented by adding noise. We envision that the proposed approach will extend the applications of CNNs for ToF prediction in a broader range.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model\'s performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

In a previous paper (Lundh, 2023), it was argued that psychological science can be seen as having three main branches, corresponding to three levels of research: research at the person level, at the population level, and at the mechanism level. The purpose of the present paper is to discuss the critique that has been raised against this model by Lamiell (2024) and Nilsson (2024) and to elaborate and specify the three-branch model in more detail. This is done by an incorporation of Nilsson\'s concept of person-sensitivity into the model, and by a clearer differentiation between the two contrasts involved: (1) the methodological focus either on individual persons or on populations of individuals; and (2) the theoretical focus either on whole-person functioning or on sub-personal mechanisms.

BACKGROUND: Randomized clinical trials (RCTs) are the gold standard for assessing treatment effectiveness; however, they have been criticized for generalizability issues such as how well trial participants represent those who receive the treatments in clinical practice. We assessed the representativeness of participants from eight RCTs for chronic spine pain in the U.S., which were used for an individual participant data meta-analysis on the cost-effectiveness of spinal manipulation for spine pain. In these clinical trials, spinal manipulation was performed by chiropractors.
METHODS: We conducted a retrospective secondary analysis of RCT data to compare trial participants\' socio-demographic characteristics, clinical features, and health outcomes to a representative sample of (a) U.S. adults with chronic spine pain and (b) U.S. adults with chronic spine pain receiving chiropractic care, using secondary data from the National Health Interview Survey (NHIS) and Medical Expenditure Panel Survey (MEPS). We assessed differences between trial and U.S. spine populations using independent t-tests for means and z-tests for proportions, accounting for the complex multi-stage survey design of the NHIS and MEPS.
RESULTS: We found the clinical trials had an under-representation of individuals from health disparity populations with lower percentages of racial and ethnic minority groups (Black/African American 7% lower, Hispanic 8% lower), less educated (No high school degree 19% lower, high school degree 11% lower), and unemployed adults (25% lower) with worse health outcomes (physical health scores 2.5 lower and mental health scores 5.3 lower using the SF-12/36) relative to the U.S. population with spine pain. While the odds of chiropractic use in the U.S. are lower for individuals from health disparity populations, the trials also under-represented these populations relative to U.S. adults with chronic spine pain who visit a chiropractor.
CONCLUSIONS: Health disparity populations are not well represented in spine pain clinical trials. Embracing key community-based approaches, which have shown promise for increasing participation of underserved communities, is needed.