Trayectoria del trastorno formal del pensamiento, desde la observación clínica al análisis computacional / Trajectory of formal thought disorder, from clinical observation to computational analysis

La adecuada comprensión de un término psicopatológico requiere, no solo del conocimiento de la alteración descrita, sino también de los contextos y conceptos a partir de los cuales fue acuñado y de la transformación de los mismos a lo largo del tiempo. En el caso del trastorno formal del pensamiento se describe su evolución desde su incorporación a la psicopatología con fines puramente descriptivos y asociado a la influencia del asociacionismo y a la idea de una dependencia directa entre pensamiento y lenguaje hasta la actualidad, en que el uso de herramientas computacionales y de hipótesis provenientes de la lingüística han promovido su uso como instrumento diagnóstico y marcador pronóstico, al tiempo que ha significado la incorporación de nueva terminología. (AU)

Properly understanding a psychopathological term requires knowledge of the disorder described, the contexts and concepts from which it was coined, and its modification over time. In the case of formal thought disorder, we describe its evolution from its incorporation into psychopathology for purely descriptive purposes and associated with the influence of associationism and the idea of a direct dependence between thought and language to the present day, in which the use of computational tools and hypotheses from linguistics have promoted its use as a diagnostic tool and prognostic marker, while simultaneously leading to the incorporation of new terminology. (AU)

Enseñanza de la Anatomía Humana en la Universidad Austral de Chile: Pasado, Presente y Futuro / Teaching of Human Anatomy at the Universidad Austral de Chile: Past, Present and Future

La presente revisión narrativa trata de caracterizar como ha sido el proceso de enseñanza de la anatomía en la Universidad Austral de Chile (UACh), presentando una visión histórica desde los inicios de la Universidad a los tiempos actuales y una reflexión sobre el futuro de la Anatomía Humana. Se describen los orígenes del Instituto de Anatomía de la UACh, primeros académicos, logros institucionales, convenios interinstitucionales; además, se detallan los métodos y estrategias educativas utilizadas en la actualidad para la enseñanza anatómica y se presenta una visión panorámica del porvenir como instituto de docencia e investigación en el campo de la Anatomía humana.

SUMMARY: This narrative review tries to characterize how the anatomy teaching process has been carried out at the Universidad Austral de Chile (UACh), presenting a historical vision from the beginnings of the University to the present time and a reflection on the future of Human Anatomy. The origins of the Human Anatomy Institute at the UACh, first academics, institutional achievements, inter-institutional agreements are described; in addition, the educational methods and strategies currently used for anatomical teaching are detailed and a panoramic vision of the future as a teaching and research institute in the field of human anatomy is presented.

[Evolution of insulin therapy: past, present, future].

2021 marks the 100th anniversary of the discovery of insulin, an event that forever changed the lives of people with diabetes mellitus. At present patients around the world experience the miracle of insulin therapy every day. A disease that used to kill children and teenagers in 2 years in 1920 has become a disease that can be controlled with a possibility to lead a long productive life. Over the past century, the great discovery of Banting, Best and Collip has forever changed the world and saved millions of lives. This review is devoted to the history of the development of insulin and its further improvement: from the moment of discovery to the present days. Various generations of insulin are considered: from animals to modern ultrashort and basal analogues. The article ends with a brief review of current trends in the development of new delivery methods and the development of new insulin molecules. Over the past century, insulin therapy has come a long way, which has significantly improved the quality of life of our patients. But research is actively continuing, including in the field of alternative methods of insulin delivery, which are more convenient for the patient, as well as in the development of «smart¼ molecules that will have a glucose-dependent effect.

Three Epochs of Artificial Intelligence in Health Care.

Importance: Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities. Observations: While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people's daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model's behavior. Prompts such as "Write this note for a specialist consultant" and "Write this note for the patient's mother" will produce markedly different content. Conclusions and Relevance: Foundation models and generative AI represent a major revolution in AI's capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

Analysis of Breast Cancer Mortality in the US-1975 to 2019.

Importance: Breast cancer mortality in the US declined between 1975 and 2019. The association of changes in metastatic breast cancer treatment with improved breast cancer mortality is unclear. Objective: To simulate the relative associations of breast cancer screening, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer with improved breast cancer mortality. Design, Setting, and Participants: Using aggregated observational and clinical trial data on the dissemination and effects of screening and treatment, 4 Cancer Intervention and Surveillance Modeling Network (CISNET) models simulated US breast cancer mortality rates. Death due to breast cancer, overall and by estrogen receptor and ERBB2 (formerly HER2) status, among women aged 30 to 79 years in the US from 1975 to 2019 was simulated. Exposures: Screening mammography, treatment of stage I to III breast cancer, and treatment of metastatic breast cancer. Main Outcomes and Measures: Model-estimated age-adjusted breast cancer mortality rate associated with screening, stage I to III treatment, and metastatic treatment relative to the absence of these exposures was assessed, as was model-estimated median survival after breast cancer metastatic recurrence. Results: The breast cancer mortality rate in the US (age adjusted) was 48/100 000 women in 1975 and 27/100 000 women in 2019. In 2019, the combination of screening, stage I to III treatment, and metastatic treatment was associated with a 58% reduction (model range, 55%-61%) in breast cancer mortality. Of this reduction, 29% (model range, 19%-33%) was associated with treatment of metastatic breast cancer, 47% (model range, 35%-60%) with treatment of stage I to III breast cancer, and 25% (model range, 21%-33%) with mammography screening. Based on simulations, the greatest change in survival after metastatic recurrence occurred between 2000 and 2019, from 1.9 years (model range, 1.0-2.7 years) to 3.2 years (model range, 2.0-4.9 years). Median survival for estrogen receptor (ER)-positive/ERBB2-positive breast cancer improved by 2.5 years (model range, 2.0-3.4 years), whereas median survival for ER-/ERBB2- breast cancer improved by 0.5 years (model range, 0.3-0.8 years). Conclusions and Relevance: According to 4 simulation models, breast cancer screening and treatment in 2019 were associated with a 58% reduction in US breast cancer mortality compared with interventions in 1975. Simulations suggested that treatment for stage I to III breast cancer was associated with approximately 47% of the mortality reduction, whereas treatment for metastatic breast cancer was associated with 29% of the reduction and screening with 25% of the reduction.