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

Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

6.8K
When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.8K
Variability: Analysis01:11

Variability: Analysis

430
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
430
Random Error01:04

Random Error

7.8K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
7.8K
Survival Tree01:19

Survival Tree

375
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
375

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

Disparities in Activation and Use of Patient Portals Among Spanish-Speaking Patients.

Applied clinical informatics·2026
Same author

Poly(glutamic acid-<i>block</i>-tyrosine) peptides designed for gastrointestinal drug adsorption.

Journal of materials chemistry. B·2026
Same author

Frontier Language Models and Optical Character Recognition Preprocessing Against Invisible Text Injection in AI Peer Review.

JAMA network open·2026
Same author

A new type of table one: showing instead of telling.

Journal of clinical epidemiology·2026
Same author

Overlooked and Undernourished: A Case Report of Scurvy Linked to Food Insecurity.

Journal of education & teaching in emergency medicine·2026
Same author

Clinical Predictors of Observation Unit Failure in Patients with Acute Heart Failure Exacerbation: A Quality Improvement Initiative.

American journal of medical quality : the official journal of the American College of Medical Quality·2026
Same journal

Immunohistochemistry (IHC) Versus Genomic Profiling in Cancer: Roles in Precision Medicine.

Cureus·2026
Same journal

Pediatric Nasal Tip Reconstruction After a Donkey Bite Using an Expanded Paramedian Forehead Flap With Conchal Cartilage Grafts: A Case Report.

Cureus·2026
Same journal

Splenic Rupture: A Delayed and Rare Complication of Colonoscopy.

Cureus·2026
Same journal

Super-refractory Status Epilepticus in Febrile Infection-Related Epilepsy Syndrome Triggered by Influenza A: A Pediatric Case Report.

Cureus·2026
Same journal

Comparative Evaluation of Serum Peroxiredoxin 2 (PRDX2), Serum Peroxiredoxin 4 (PRDX4), and Plasma Methylated Septin 9 (mSEPT9) Levels Against Conventional Biomarkers for Early Detection of Colorectal Cancer: A Study Protocol.

Cureus·2026
Same journal

Inspiratory Muscle Training for Patients With Chronic Obstructive Pulmonary Disease: A Narrative Review.

Cureus·2026
查看所有相关文章

相关实验视频

Updated: Jan 11, 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

1.0K

使用领先的大型语言模型在紧急诊断准确性中试点温度驱动的变量.

Philip C Jarrett1, Jared Hill1, Marshall Howell1

  • 1Emergency Medicine, University of Texas Southwestern Medical Center, Dallas, USA.

Cureus
|November 14, 2025
PubMed
概括
此摘要是机器生成的。

在像GPT-4o这样的大型语言模型 (LLM) 中降低温度参数可以提高紧急医疗病例的诊断准确性. 较低的温度提高了临床AI应用的可靠性和一致性.

关键词:
医学中的人工智能临床决策支持 临床决策支持临床信息学 临床信息学诊断的准确性 诊断的准确性紧急医疗 紧急医疗

更多相关视频

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

791
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

1.3K

相关实验视频

Last Updated: Jan 11, 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

1.0K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

791
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

1.3K

科学领域:

  • 人工智能的人工智能
  • 医学诊断 医学诊断 医学诊断
  • 临床决策支持 临床决策支持

背景情况:

  • 大型语言模型 (LLM) 使用"温度"参数来控制输出随机性.
  • 这一参数对临床诊断准确性的影响,特别是在紧急医疗中,尚不清楚.
  • 了解温度的影响对于医疗保健中可靠的人工智能至关重要.

研究的目的:

  • 评估温度参数对GPT-4o在紧急医疗病例的诊断准确度的影响.
  • 评估温度如何影响多次代的诊断分歧和一致性.
  • 通过使用LLMs来确定可靠的临床诊断任务的最佳温度设置.

主要方法:

  • 一项基于模拟的研究使用了四个具有挑战性的紧急医疗病例.
  • 在五个温度设置 (0.0-1.0) 中,GPT-4o产生了10,000个差异诊断,并且有/没有体检结果.
  • 诊断准确性与黄金标准进行了基准测试;诊断分歧是通过生成的独特诊断来衡量的.

主要成果:

  • 在0.0温度下,GPT-4o实现了100%的领先诊断准确度,在1.0温度下降到89.4%.
  • 较高的温度显著增加了诊断的不准确性和差异 (483%的增加从0.0到1.0).
  • 病例对温度的敏感性各不相同,一些诊断严重受到体检数据排除的影响.

结论:

  • 在GPT-4o中增加温度参数系统地降低了诊断准确度和紧急医疗场景中的一致性.
  • 较低的温度设置 (例如0.0) 与更高的准确性和可靠性有关,这使得它们在临床使用中具有潜在的优势.
  • 对温度设置的透明报告对于临床AI研究中的可重复性至关重要.