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Related Concept Videos

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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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.
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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.
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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 and Cognition01:27

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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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Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
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A Large Language Model Workflow for Auditable Brain Abscess Risk Stratification and Pre-residency Scholarship: A

Amir Akhavan1, Swapan Nath2

  • 1Medicine, Anne Burnett Marion School of Medicine at Texas Christian University, Fort Worth, USA.

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|February 2, 2026
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Summary
This summary is machine-generated.

This study introduces a mentored framework for using large language models (LLMs) in medical education to teach artificial intelligence (AI) literacy. It demonstrates how LLMs can support rigorous scholarly work and develop auditable AI use skills for trainees.

Keywords:
artificial intelligencebrain abscessevidence synthesislarge language modelsmedical educationmentorshipprompt engineeringrisk stratificationundergraduate medical education

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Area of Science:

  • Medical Education
  • Artificial Intelligence in Medicine
  • Scholarly Workflows

Background:

  • Medical trainees need transparent methods for integrating large language models (LLMs) into scholarly work.
  • Existing artificial intelligence (AI) literacy curricula lack auditable frameworks for LLM use.
  • Developing structured AI use skills is crucial for pre-residency readiness.

Purpose of the Study:

  • To describe a mentored educational framework for teaching AI literacy and structured LLM use to medical trainees.
  • To transform a clinical case report into a reproducible, LLM-supported risk-stratification model.
  • To foster rigor, verification, and transparent AI engagement in scholarly activities.

Main Methods:

  • A de-identified brain abscess case was reconstructed using structured clinical variables.
  • LLM interactions were logged in a standardized prompt ledger, capturing rationale and decisions.
  • A provisional Neurologic Deterioration in Brain Abscess Score (NDBAS v0.1) was developed based on literature-supported indicators.

Main Results:

  • The LLM-assisted synthesis accelerated evidence review compared to manual methods.
  • The framework produced three curriculum artifacts: a case appendix, prompt ledger, and variable dictionary.
  • The index case received an NDBAS v0.1 score of 5, indicating moderate risk.

Conclusions:

  • This structured, audit-ready LLM workflow enhances AI literacy and scholarly output for medical trainees.
  • The 'Prompt Ledger plus ask-verify-revise' pattern facilitates transparent evidence synthesis and conceptual model-building.
  • Mentored LLM workflows, with appropriate governance, can prepare learners for responsible AI engagement in clinical training.