2013临床试验方案规范指南（SPIRIT）及其解读

SPIRIT（Standard Protocol Items: Recommendations for Interventional Trials）声明于2013年在《Annals of Internal Medicine》上发表。该声明以清单条目的形式,并配套同年发表的《SPIRIT 2013解读和说明》,旨在促进高质量临床试验方案的形成。本文对SPIRIT的各项条目进行解读,并提出重点注意事项,为临床试验方案的形成及整个临床试验的顺利进行提供依据。     

How Can Philosophy Be a True Cognitive Science Discipline?

Although philosophy has been only a minor contributor to cognitive science to date, this paper describes two projects in naturalistic philosophy of mind and one in naturalistic philosophy of science that have been pursued during the past 30 years and that can make theoretical and methodological contributions to cognitive science. First, stances on the mind–body problem (identity theory, functionalism, and heuristic identity theory) are relevant to cognitive science as it negotiates its relation to neuroscience and cognitive neuroscience. Second, analyses of mental representations address both their vehicles and their contents; new approaches to characterizing how representations have content are particularly relevant to understanding the relation of cognitive agents to their environments. Third, the recently formulated accounts of mechanistic explanation in philosophy of science both provide perspective on the explanatory project of cognitive science and may offer normative guidance to cognitive science (e.g., by providing perspective on how multiple disciplinary perspectives can be integrated in understanding a given mechanism).     

Intelligent dialogue based on statistical models of clinical decision-making

The independence Bayesian model has been used widely in computer programs designed to support clinical decision-making. A reasoning strategy has been developed to enable these programs to conduct clinically pertinent dialogue and explain their reasoning. It has been implemented in a program for the diagnosis of acute abdominal pain based on the Bayesian model of de Dombal et al. Several features of the dialogue design have been adopted from artificial intelligence research, including shared initiative and critiquing. The program adopts a flexible goal-driven strategy, attempting to confirm the clinician's diagnosis or rule out the likeliest alternative. Symptoms and signs are selected in order of their expected weights of evidence in favour of the hypothesized disease.     

针灸专业研究生教育与经典理论学习

通过对针灸专业研究生教育现状以及对他们毕业后在工作中所反映问题的分析,结合对教育部研究生工作办公室推荐研究生教学用书<针灸经典理论阐释>反复学习的体会,探讨针灸专业研究生教育中针灸经典理论学习的意义和方法.     

乳腺疾病钼靶X线征象探讨

目的明确乳腺疾病的钼靶摄影征象，探讨钼靶在早期乳腺癌诊断中的价值。方法总结分析我院从512例乳腺疾病钼靶X线摄影中检出，并经手术和病理证实为乳腺肿瘤35例，其中乳腺良性肿瘤26例，乳腺癌9例。结果9例乳腺癌X线显示肿块影5例，有钙化灶1例。良性肿瘤X线征象为密度均匀、边缘光滑、椭圆或圆形肿块，周围有环形透亮带。结论钼靶X线乳腺摄影是诊断乳腺肿瘤的首选方法，特别是乳腺癌的主要直接征象和间接征象在乳腺癌的早期诊断中具有重要意义。     

Searching for General Principles in Cognitive Performance: Reply to Commentators

The commentators expressed concerns regarding the relevance and value of non‐computational non‐symbolic explanations of cognitive performance. But what counts as an “explanation” depends on the pre‐theoretical assumptions behind the scenes of empirical science regarding the kinds of variables and relationships that are sought out in the first place, and some of the present disagreements stem from incommensurate assumptions. Traditional cognitive science presumes cognition to be a decomposable system of components interacting according to computational rules to generate cognitive performances (i.e., component‐dominant dynamics). We assign primacy to interaction‐dominant dynamics among components. Though either choice can be a good guess before the fact, the primacy of interactions is now supported by much recent empirical work in cognitive science. Consequently, in the main, the commentators have failed so far to address the growing evidence corroborating the theory‐driven predictions of complexity science.     

Self-organization takes time too

Four articles in this issue of topiCS (volume 4, issue 1) argue against a computational approach in cognitive science in favor of a dynamical approach. I concur that the computational approach faces some considerable explanatory challenges. Yet the dynamicists' proposal that cognition is self-organized seems to only go so far in addressing these challenges. Take, for instance, the hypothesis that cognitive behavior emerges when brain and body (re-)configure to satisfy task and environmental constraints. It is known that for certain systems of constraints, no procedure can exist (whether modular, local, centralized, or self-organized) that reliably finds the right configuration in a realistic amount of time. Hence, the dynamical approach still faces the challenge of explaining how self-organized constraint satisfaction can be achieved by human brains and bodies in real time. In this commentary, I propose a methodology that dynamicists can use to try to address this challenge.