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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...

You might also read

Related Articles

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

Sort by
Same author

Using Serious Games to Increase the Implementation of Trauma Triage Guidelines: A Randomized Clinical Trial.

JAMA·2026
Same author

Core outcome set for liver trauma: a consensus approach using modified Delphi methodology.

Trauma surgery & acute care open·2026
Same author

American Association for the Surgery of Trauma-World Society of Emergency Surgery guidelines on the diagnosis and management of major thoracic vascular injuries.

The journal of trauma and acute care surgery·2026
Same author

American Association for the Surgery of Trauma-World Society of Emergency Surgery Guidelines on the diagnosis and management of cervical vascular injuries.

The journal of trauma and acute care surgery·2026
Same author

Liver injury: What you need to know.

The journal of trauma and acute care surgery·2025
Same author

Examining air medical transport in interfacility emergency general surgery transfers to a quaternary center.

The journal of trauma and acute care surgery·2025
Same journal

Article.

The Journal of trauma·2014
Same journal

Article.

The Journal of trauma·2014
Same journal

Program schedule for the sixty-fifth annual meeting of the american association for the surgery of trauma.

The Journal of trauma·2014
Same journal

Letters to the editor.

The Journal of trauma·2014
Same journal

Posttraumatic brachial plexitis.

The Journal of trauma·2011
Same journal

Incidental findings in focused assessment with sonography for trauma in hemodynamically stable blunt trauma patients: speaking about cost to benefit.

The Journal of trauma·2011
See all related articles

Related Experiment Video

Updated: Jun 18, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Preventability classification in mortality cases: a reliability study.

Michael D Pasquale1, Andrew B Peitzman,

  • 1Department of Surgery, Lehigh Valley Hospital, Allentown, Pennsylvania 18105-1556, USA.

The Journal of Trauma
|November 11, 2009
PubMed
Summary
This summary is machine-generated.

The Pennsylvania Outcomes and Performance Improvement Measurement System (POPIMS) software showed moderate reliability for trauma mortality classification. Differences between institutional and reviewer classifications indicate a need for more objective criteria in trauma outcome reporting.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Experiment Videos

Last Updated: Jun 18, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Medical Informatics
  • Trauma Surgery
  • Quality Improvement

Background:

  • The Pennsylvania Trauma Systems Foundation developed the Pennsylvania Outcomes and Performance Improvement Measurement System (POPIMS) software for consistent trauma center outcomes reporting.
  • This study aimed to evaluate the inter-rater reliability of the POPIMS software for classifying trauma mortality preventability.

Purpose of the Study:

  • To assess the consistency and reliability of mortality classification using the POPIMS software among trauma centers in Pennsylvania.
  • To identify discrepancies between institutional and reviewer classifications of mortality preventability.

Main Methods:

  • Trauma centers submitted preventable, potentially preventable, and nonpreventable mortality reports using POPIMS.
  • Reports were blinded and randomly selected for review by trauma directors.
  • Institutional classification (IC) was compared with reviewer classification (RC) using Chi-square tests and Cronbach's alpha for inter-rater reliability.

Main Results:

  • Twenty-eight surgeons reviewed 34 cases; reviewer classification (RC) significantly differed from institutional classification (IC) (p < 0.001).
  • Factors contributing to mortality varied between IC and RC reviews across different preventability classes.
  • A moderate level of inter-rater reliability was observed, with Cronbach's alpha at 0.64.

Conclusions:

  • The POPIMS system, a statewide performance improvement initiative, demonstrated moderate inter-rater reliability for mortality classification.
  • Significant differences highlight the need for more objective criteria in classifying trauma mortality preventability.
  • Further development should explore additional outcomes parameters beyond preventability classification.