Depression is an eminently treatable disorder that responds to psychotherapy or medications; the efficacy of each has been established in hundreds of controlled trials. Nonetheless, the prevalence of depression has increased in recent years despite the existence of efficacious treatments-a phenomenon known as the treatment-prevalence paradox. We consider several possible explanations for this paradox, which range from a misunderstanding of the very nature of depression, inflated efficacy of the established treatments, and a lack of access to efficacious delivery of treatments. We find support for each of these possible explanations but especially the notion that large segments of the population lack access to efficacious treatments that are implemented as intended. We conclude by describing the potential of using lay therapists and digital technologies to overcome this lack of access and to reach historically underserved populations and simultaneously guarantee the quality of the interventions delivered.

OBJECTIVE: Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials.
RESULTS: Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization.
CONCLUSIONS: Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.

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.

Literature shows heterogeneous age-standardized dementia incidence rates across US Asian American, Native Hawaiian, and Pacific Islanders (AANHPI), but no estimates of population-representative dementia incidence exist due to lack of AANHPI longitudinal probability samples. We compared harmonized characteristics between AANHPI Kaiser Permanente Northern California members (KPNC cohort) and the target population of AANHPI 60+ with private or Medicare insurance using the California Health Interview Survey. We used stabilized inverse odds of selection weights (sIOSW) to estimate ethnicity-specific crude and age-standardized dementia incidence rates and cumulative risk by age 90 in the target population. Differences between the KPNC cohort and target population varied by ethnicity. sIOSW eliminated most differences in larger ethnic groups; some differences remained in smaller groups. Estimated crude dementia incidence rates using sIOSW (versus unweighted) were similar in Chinese, Filipinos, Pacific Islanders and Vietnamese, and higher in Japanese, Koreans, and South Asians. Unweighted and weighted age-standardized incidence rates differed for South Asians. Unweighted and weighted cumulative risk were similar for all groups. We estimated the first population-representative dementia incidence rates and cumulative risk in AANHPI ethnic groups. We encountered some estimation problems and weighted estimates were imprecise, highlighting challenges using weighting to extend inferences to target populations.

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.

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A common research protocol in cognitive neuroscience is to train subjects to perform deliberately designed experiments while recording brain activity, with the aim of understanding the brain mechanisms underlying cognition. However, how the results of this protocol of research can be applied in technology is seldom discussed. Here, I review the studies on time processing of the brain as examples of this research protocol, as well as two main application areas of neuroscience (neuroengineering and brain-inspired artificial intelligence). Time processing is a fundamental dimension of cognition, and time is also an indispensable dimension of any real-world signal to be processed in technology. Therefore, one may expect that the studies of time processing in cognition profoundly influence brain-related technology. Surprisingly, I found that the results from cognitive studies on timing processing are hardly helpful in solving practical problems. This awkward situation may be due to the lack of generalizability of the results of cognitive studies, which are under well-controlled laboratory conditions, to real-life situations. This lack of generalizability may be rooted in the fundamental unknowability of the world (including cognition). Overall, this paper questions and criticizes the usefulness and prospect of the abovementioned research protocol of cognitive neuroscience. I then give three suggestions for future research. First, to improve the generalizability of research, it is better to study brain activity under real-life conditions instead of in well-controlled laboratory experiments. Second, to overcome the unknowability of the world, we can engineer an easily accessible surrogate of the object under investigation, so that we can predict the behavior of the object under investigation by experimenting on the surrogate. Third, the paper calls for technology-oriented research, with the aim of technology creation instead of knowledge discovery.

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.

When analyzing a selected sample from a general population, selection bias can arise relative to the causal average treatment effect (ATE) for the general population, and also relative to the ATE for the selected sample itself. We provide simple graphical rules that indicate: (1) if a selected-sample analysis will be unbiased for each ATE; (2) whether adjusting for certain covariates could eliminate selection bias. The rules can easily be checked in a standard single-world intervention graph. When the treatment could affect selection, a third estimand of potential scientific interest is the \"net treatment difference\", namely the net change in outcomes that would occur for the selected sample if all members of the general population were treated versus not treated, including any effects of the treatment on which individuals are in the selected sample . We provide graphical rules for this estimand as well. We decompose bias in a selected-sample analysis relative to the general-population ATE into: (1) \"internal bias\" relative to the net treatment difference; (2) \"net-external bias\", a discrepancy between the net treatment difference and the general-population ATE. Each bias can be assessed unambiguously via a distinct graphical rule, providing new conceptual insight into the mechanisms by which certain causal structures produce selection bias.