Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated hypertension...
Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration01:28

Drug Dosing in Renal Diseases: Estimation of Glomerular Filtration Rate Based on Serum Creatinine Concentration

Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area. This equation is...
Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Previsit Preparation for Shared Decision-Making in Lung Cancer Screening in Primary Care Using a Paper Decision Aid and an Automated Text Messaging Program: Quasi-Experimental Pilot Study.

JMIR formative research·2025
Same author

Incorporating the video communication assessment for error disclosure in residency curricula: a mixed methods study of faculty perceptions.

Frontiers in health services·2025
Same author

Intent to Test for COVID-19 in the Postpandemic Era.

JAMA network open·2025
Same author

Reasons for COVID-19 vaccination late in the pandemic: A qualitative study.

Vaccine·2025
Same author

A New CAHPS Measure of Patient Experiences With Mental Health and Substance Use Care.

Health services research·2025
Same author

Health Concerns of Youths From Historically Marginalized Communities During the Postacute Phase of COVID-19.

JAMA network open·2025

Related Experiment Videos

Are population-based diabetes models useful for individual risk estimation?

Barry G Saver1, J Lee Hargraves, Kathleen M Mazor

  • 1Department Family Medicine and Community Health, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655, USA. Barry.Saver@umassmed.edu

Journal of the American Board of Family Medicine : JABFM
|July 9, 2011
PubMed
Summary

Two major diabetes risk prediction models, the UKPDS outcomes model and the Diabetes PHD model, generated substantially different risk estimates for cardiovascular events and complications. Understanding model uncertainty is crucial for clinical decision-making.

Related Experiment Videos

Area of Science:

  • Medical Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Predictive models are integral to diabetes management guidelines and clinical decision-making.
  • Two prominent diabetes risk prediction models, the UKPDS outcomes model and the Diabetes Personal Health Decisions (PHD) model, are widely utilized.
  • This study critically evaluates the comparative performance of these two models.

Purpose of the Study:

  • To compare the 10-year and 20-year risk predictions for major diabetes complications between the UKPDS and Diabetes PHD models.
  • To assess the discrepancies in predicted risks for myocardial infarction, stroke, amputation, blindness, and renal failure.
  • To evaluate how demographic factors influence model predictions.

Main Methods:

  • A simulation study design was employed.
  • Predictions for 10-year and 20-year risks of specific diabetes-related outcomes were generated for representative test cases.
  • Model predictions were compared, including confidence intervals and differences across demographic groups.

Main Results:

  • The Diabetes PHD model generally predicted higher risks for myocardial infarction and stroke, especially for 20-year outcomes.
  • The UKPDS model predicted significantly higher risks for amputation and blindness compared to the Diabetes PHD model.
  • Substantial discrepancies were observed in renal failure predictions, and risk predictions varied considerably across different demographic groups (e.g., race, sex).

Conclusions:

  • The UKPDS and Diabetes PHD models yield markedly different risk predictions for diabetes complications.
  • Significant uncertainty and potential for misclassification exist within these predictive models.
  • Clinicians and patients must be aware of these differences and uncertainties when using risk estimates for informed decision-making.