Those who comment on modern scientific institutions are often quick to praise institutional structures that leave scientists to their own devices. These comments reveal an underlying presumption that scientists do best when left alone-when they operate in what we call the 'scientific state of nature'. Through computer simulation, we challenge this presumption by illustrating an inefficiency that arises in the scientific state of nature. This inefficiency suggests that one cannot simply presume that science is most efficient when institutional control is absent. In some situations, actively encouraging unpopular, risky science would improve scientific outcomes.
IMPORTANCE Rib fractures are sustained by nearly 15% of patients who experience trauma and are associated with significant morbidity and mortality. Evidence-based practice (EBP) rib fracture management guidelines and treatment algorithms have been published. However, few studies have evaluated trauma center adherence to EBP or the clinical outcomes of each practice within a national cohort. OBJECTIVE To examine adherence to 6 EBPs for rib fractures across US trauma centers and the association with in-hospital mortality.
Current fears of a "reproducibility crisis" have led researchers, sources of scientific funding, and the public to question both the efficacy and trustworthiness of science (1, 2). Suggested policy changes have been focused on statistical problems, such as p-hacking, and issues of experimental design and execution (3, 4). However, "reproducibility" is a broad concept that includes a number of issues (5) (see also www.pnas. org/improving_reproducibility). Furthermore, reproducibility failures occur even in fields such as mathematics or computer science that do not have statistical problems or issues with experimental design. Most importantly, these proposed policy changes ignore a core feature of the process of scientific inquiry that
Many scientific research programs aim to learn the causal structure of real world phenomena. This learning problem is made more difficult when the target of study cannot be directly observed. One strategy commonly used by social scientists is to create measurable “indicator” variables that covary with the latent variables of interest. Before leveraging the indicator variables to learn about the latent variables, however, one needs a measurement model of the causal relations between the indicators and their corresponding latents. These measurement models are a special class of Bayesian networks. This paper addresses the problem of reliably inferring measurement models from measured indicators, without prior knowledge of the causal relations or the number of latent variables. We present a provably correct novel algorithm, FindOneFactorClusters (FOFC), for solving this inference problem. Compared to other state of the art algorithms, FOFC is faster, scales to larger sets of indicators, and is more reliable at small sample sizes. We also present the first correctness proofs for this problem that do not assume linearity or acyclicity among the latent variables.
Background:
Anxiety and depression (“internalizing”) disorders occur in approximately 50% of patients with alcohol use disorder (AUD) and mark a two-fold increase in the rate of relapse in the months following treatment. In a previous study using network modeling, we found that perceived stress and drinking to cope (DTC) with negative affect were central to maintaining network associations between internalizing psychopathology (anxiety and depression) and drinking in comorbid individuals. Here, we extend this approach to a causal framework.
Methods:
Measures of internalizing psychopathology, drinking urges/behavior, abstinence self-efficacy, and DTC were obtained from 362 adult AUD treatment patients who had a co-occurring anxiety disorder. Data were analyzed using a machine-learning algorithm (“Greedy Fast Causal Inference; GFCI) that infers paths of causal influence while identifying potential influences associated with unmeasured (“latent”) variables.
Results:
Drinking to cope with negative affect served as a central hub for two distinct causal paths leading to drinking behavior, 1) a direct syndromic pathway originating with social anxiety and 2) an indirect stress pathway originating with perceived stress.
Conclusions:
Findings expand the field’s knowledge of the paths of influence that lead from internalizing disorder to drinking in AUD as shown by the first application in psychopathology of a powerful network analysis algorithm (GFCI) to model these causal relationships.
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