Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Pre-Procedural Guidelines for Assessing Blood Pressure01:10

Pre-Procedural Guidelines for Assessing Blood Pressure

Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the patient.
Regulation of Stroke Volume01:27

Regulation of Stroke Volume

The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
Neural Regulation of Blood Pressure01:18

Neural Regulation of Blood Pressure

The neural regulation of blood pressure involves intricate interactions between the autonomic nervous system (ANS) and cardiovascular system, ensuring adequate perfusion of tissues. This regulation primarily occurs through baroreceptor and chemoreceptor reflexes, involving both short-term and long-term mechanisms.
Baroreceptor Reflex
Baroreceptors, located in the carotid sinuses and aortic arch, detect changes in blood pressure. When blood pressure rises, these stretch-sensitive receptors...
Autoregulation of Blood Flow01:17

Autoregulation of Blood Flow

Autoregulation mechanisms are characterized by their inherent capacity for self-regulation without necessitating specific nervous stimulation or endocrine control. These mechanisms facilitate the adjustment of blood flow and, therefore, perfusion specific to each tissue region. This self-regulation encompasses chemical signals and myogenic controls.
Chemical Signaling in Autoregulation
Chemical signaling operates at the precapillary sphincter level, inciting either contraction or relaxation.

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Non-Alcoholic Beverage Intake and Risk of Cutaneous Malignancies: A Systematic Review.

Journal of cutaneous medicine and surgery·2026
Same author

Efficacy and Safety of Spironolactone for Adolescent Acne Vulgaris.

International journal of dermatology·2026
Same author

Off-Label Use of Deucravacitinib in Dermatology: A Systematic Review.

Journal of cutaneous medicine and surgery·2025
Same author

Lichen Planopilaris and the Lichen Planopilaris Mimickers: A Systematic Review.

International journal of dermatology·2025
Same author

Constitutional Mismatch Repair Deficiency: Scoping Review of a Cancer-Predisposition Syndrome With Distinctive Cutaneous Findings.

Pediatric dermatology·2025
Same author

Thyroid cancer quality of care indicators: A scoping review.

American journal of surgery·2025
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Healthy Lifestyles and Self-Improvement Videos on YouTube: A Thematic Analysis of Teen-Targeted Social Media Content.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
查看所有相关文章

相关实验视频

Updated: Jun 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

使用大型语言模型自动判断心血管事件.

Sonish Sivarajkumar1,2, Kimia Ameri1, Chuqin Li1

  • 1Advanced Analytics and Data Sciences, Eli Lilly and Company, USA.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
PubMed
概括
此摘要是机器生成的。

在使用大型语言模型 (LLM) 的临床试验中自动化心血管死亡判断显著加快了过程,并减少了变化. 这种AI框架提高了事件分类的准确性和透明度.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K

相关实验视频

Last Updated: Jun 7, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.7K

科学领域:

  • 人工智能的人工智能
  • 临床试验 临床试验
  • 医疗信息学 医疗信息学

背景情况:

  • 在临床试验中判断心血管事件至关重要,但传统上是手动的,导致延迟,不一致性和高成本.
  • 手动审查临床文件以判断事件是耗时的,容易出现人为错误.

研究的目的:

  • 开发和评估使用大型语言模型 (LLM) 的两阶段框架,以自动化在临床试验中判断心血管死亡.
  • 提高心血管事件裁决的效率,一致性和透明度.

主要方法:

  • 一个采用大型语言模型 (LLM) 进行自动裁决的两阶段框架.
  • 第一个阶段:简单的LLM从非结构化的临床文件中提取结构化的证据 (事件,否定,日期,跨度).
  • 第二阶段:一个思维树的判断者使用临床终点委员会 (CEC) 的指导方针进行分类和推理生成.

主要成果:

  • 基于LLM的框架在从临床文档中提取结构化证据时实现了高精度 (0.96) 和F1得分 (0.82).
  • 判定阶段使用GPT-4思维树证明了0.68的准确性,超过了基线总结者加判定者的方法.
  • CLEART评分 (0.67) 量化了理由质量,确定了时间推理和相关性作为需要改进的领域.

结论:

  • 使用LLMs对心血管死亡的自动判断提供了一个有希望的解决方案,以提高临床试验的效率和减少临床试验的变化.
  • 拟议的框架提供了一个可审计的理由,提高了裁决过程的透明度.
  • 需要进一步改进,以优化自动化裁决系统中的时间推理和相关性.