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Higher Mental Functions of the Brain: Language01:10

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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Language01:16

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Components of Language01:24

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Video Experimental Relacionado

Updated: Feb 12, 2026

Involving Individuals with Developmental Language Disorder and Their Parents/Carers in Research Priority Setting
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Un enfoque de modelo de lenguaje grande para la evaluación de la escala de estado funcional.

Blake Martin1,2,3, Anna M Janas1,3, Kristen R Miller1,3

  • 1Section of Critical Care Medicine, Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
|February 11, 2026
PubMed
Resumen

Un modelo de inteligencia artificial (IA) afinado, FSS-AI, puede estimar las puntuaciones de la Escala de Estado Funcional (FSS) en niños críticamente enfermos. La IA mostró un acuerdo moderado con las puntuaciones manuales y pudo identificar FSS normales versus anormales.

Palabras clave:
cuidado de la salud cuidado de la salud.Modelos de lenguaje de gran tamaño.Procesamiento de lenguaje natural.Evaluación de la evaluación de resultados.

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Área de la Ciencia:

  • La inteligencia artificial en la medicina.
  • Cuidados críticos pediátricos en cuidados intensivos pediátricos.
  • Informática de la salud Informática de la salud.

Sus antecedentes:

  • La estimación del estado funcional en niños con enfermedades críticas es crucial para la planificación de la atención y la evaluación de los resultados.
  • La Escala de Estado Funcional (FSS) es una herramienta común, pero la puntuación manual puede llevar mucho tiempo y ser subjetiva.
  • Aprovechar la IA avanzada como GPT-4o ofrece el potencial para una puntuación FSS automatizada y eficiente.

Objetivo del estudio:

  • Desarrollar y evaluar un modelo de inteligencia artificial (IA) 4o transformador generativo preentrenado (GPT) afinado, llamado FSS-AI, para estimar las puntuaciones de FSS en niños con enfermedades críticas.
  • Evaluar la precisión y la concordancia de las puntuaciones FSS generadas por IA en comparación con las puntuaciones asignadas manualmente en diferentes puntos de tiempo de hospitalización.

Principales métodos:

  • Se realizó un análisis secundario de una cohorte prospectiva de niños con ventilación mecánica (de 1 mes a 18 años).
  • Las notas de los pacientes desde el inicio, la transferencia a la PICU y el alta hospitalaria se utilizaron para entrenar y probar el modelo personalizado GPT-4o (FSS-AI).
  • El rendimiento de FSS-AI se evaluó comparando sus puntuaciones generadas con las puntuaciones de FSS manuales determinadas prospectivamente utilizando métricas ponderadas de Kappa y precisión de Cohen.

Principales resultados:

  • FSS-AI analizó 428 notas de pacientes, lo que demuestra un acuerdo moderado con las puntuaciones manuales de FSS al inicio (Kappa = 0.59) y la descarga (Kappa = 0.51).
  • El modelo mostró una buena discriminación entre las puntuaciones normales y anormales de FSS, con la mayor precisión (0,90) y PPV (0,95) al inicio.
  • FSS-AI identificó nuevas morbilidades en el alta con una precisión del 75% y una sensibilidad del 56%.

Conclusiones:

  • Un modelo personalizado GPT-4o (FSS-AI) puede estimar efectivamente las puntuaciones de FSS en niños en estado crítico en varias etapas de hospitalización.
  • La herramienta de IA muestra un acuerdo moderado con la puntuación manual y puede diferenciar entre el estado funcional normal y anormal.
  • FSS-AI demuestra el potencial como una herramienta eficiente para evaluar el estado funcional e identificar nuevas morbilidades en entornos de cuidados intensivos pediátricos.