Marr and Reductionism

David Marr's three-level method for completely understanding a cognitive system and the importance he attaches to the computational level are so familiar as to scarcely need repeating. Fewer seem to recognize that Marr defends his famous method by criticizing the “reductionistic approach.” This sets up a more interesting relationship between Marr and reductionism than is usually acknowledged. I argue that Marr was correct in his criticism of the reductionists of his time—they were only describing (cellular activity), not explaining (cognitive functions). But a careful metascientific account of reductionistic neuroscience over the past two decades reveals that Marr's criticisms no longer have force. Contemporary neuroscience now explains cognition directly, although in a fashion—causal-mechanistically—quite different than Marr recommended. So while Marr was correct to reject the reductionism of his day and offer an alternative method for genuinely explaining cognition, contemporary cognitive scientists now owe us a new defense of Marr's famous method and the advantages of its explanations over the type now pursued successfully in current reductionist neuroscience. There are familiar reasons for thinking that this debt will not be paid easily.     

经后颅窝乙状窦后入路三叉神经微血管减压术的快速康复外科护理

目的 探讨快速康复外科 （FTS）理念在经后颅窝乙状窦后入路三叉神经微血管减压术围术期患者护理中的应用效果。方法 选择2018年1月~2019年1月我院收治并行经后颅窝乙状窦后入路三叉神经微血管减压患者100例，随机分成观察组和对照组，每组50例。对照组应用常规方法进行围术期护理，观察组应用FTS理念进行围术期护理。比较两组焦虑情况、术后并发症发生率、平均住院日及平均住院费用。结果 观察组轻度焦虑多于对照组，中、重度焦虑少于对照组（P＜0.05）；观察组术后并发症发生率低于对照组[恶心呕吐（4.00% vs 16.00%）、尿路感染（4.00% vs 18.00%）、颅内感染（0 vs 8.00%）、颅内血肿（2.00% vs 14.00%）和术后应激性疼痛（16.00% vs 34.00%）]，差异有统计学意义（P＜0.05）；观察组平均住院日和平均住院费用均低于对照组[（9.13±1.14）d vs（12.44±0.89）d]；[（2.15±0.66）万元 vs （3.05±0.61）万元]，差异有统计学意义（P＜0.05）。结论 应用FTS理念能缓解经后颅窝乙状窦后入路三叉神经微血管减压术患者术前紧张恐惧的心理，减少术后并发症，缩短患者住院日，降低住院费用，护理效果较好。     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

The case for a medication first approach to the treatment of opioid use disorder

Background: The opioid addiction and overdose crisis continues to ravage communities across the U.S. Maintenance pharmacotherapy using buprenorphine or methadone is the most effective intervention for Opioid Use Disorder (OUD), yet few have immediate and sustained access to these medications. Objectives: To address lack of medication access for people with OUD, the Missouri Department of Mental Health began implementing a Medication First (Med First) treatment approach in its publicly-funded system of comprehensive substance use disorder treatment programs. Methods: This Perspective describes the four principles of Med First, which are based on evidence-based guidelines. It draws conceptual comparisons between the Housing First approach to chronic homelessness and the Med First approach to pharmacotherapy for OUD, and compares state certification standards for substance use disorder (SUD) treatment (the traditional approach) to Med First guidelines for OUD treatment. Finally, the Perspective details how Med First principles have been practically implemented. Results: Med First principles emphasize timely access to maintenance pharmacotherapy without requiring psychosocial services or discontinuation for any reason other than harm to the client. Early results regarding medication utilization and treatment retention are promising. Feedback from providers has been largely favorable, though clinical- and system-level obstacles to effective OUD treatment remain. Conclusion: Like the Housing First model, Medication First is designed to decrease human suffering and activate the strengths and capacities of people in need. It draws on decades of research and facilitates partnerships between psychosocial and medical treatment providers to offer effective and life-saving care to persons with OUD.