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

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
Prediction Intervals01:03

Prediction Intervals

3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.4K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.4K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.4K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

1.2K
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
1.2K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

11.0K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
11.0K
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

14.8K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
14.8K

You might also read

Related Articles

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

Sort by
Same author

From Langmuir-Blodgett to Grafted Films.

Langmuir : the ACS journal of surfaces and colloids·2020
Same author

Disruption of Asymmetric Lipid Bilayer Models Mimicking the Outer Membrane of Gram-Negative Bacteria by an Active Plasticin.

Langmuir : the ACS journal of surfaces and colloids·2017
Same author

Erratum to: Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5).

Clinical and translational allergy·2017
Same author

Building Bridges for Innovation in Ageing: Synergies between Action Groups of the EIP on AHA.

The journal of nutrition, health & aging·2016
Same author

Scaling up strategies of the chronic respiratory disease programme of the European Innovation Partnership on Active and Healthy Ageing (Action Plan B3: Area 5).

Clinical and translational allergy·2016
Same author

The European Union Geriatric Medicine Society (EUGMS) Working Group on «Frailty in Older Persons».

The Journal of frailty & aging·2016

Related Experiment Video

Updated: Feb 10, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

924

Predicting bacteremia in older patients

P Pfitzenmeyer1, H Decrey, R Auckenthaler

  • 1University Geriatric Hospital, Geneva, Switzerland.

Journal of the American Geriatrics Society
|March 1, 1995
PubMed
Summary

This study identified key clinical predictors for bacteremia in hospitalized geriatric patients. A new risk score model proved more effective than subjective judgment for early bacteremia detection and treatment.

Area of Science:

  • Geriatric Medicine
  • Infectious Diseases
  • Clinical Prediction Models

Background:

  • Bacteremia poses a significant risk in hospitalized geriatric patients.
  • Early and accurate diagnosis is crucial for effective treatment and improved outcomes.
  • Current subjective clinical judgment may be insufficient for efficient bacteremia recognition.

Purpose of the Study:

  • To identify clinical predictors of bacteremia in hospitalized geriatric patients.
  • To develop an individual risk score for bacteremia prediction.
  • To offer an alternative to subjective clinical assessment for early bacteremia recognition and treatment.

Main Methods:

  • Prospective study conducted over 16 months at a University Geriatric Hospital.
  • Inclusion of 438 geriatric patients (age ≥ 62) with suspected bacteremia (558 episodes).

More Related Videos

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

5.9K
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.8K

Related Experiment Videos

Last Updated: Feb 10, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

924
Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients
07:42

Patient-Derived Tumor Explants As a "Live" Preclinical Platform for Predicting Drug Resistance in Patients

Published on: February 7, 2021

5.9K
Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
07:25

Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia

Published on: September 22, 2020

3.8K

  • Data collection via a precoded protocol, including clinical and biological data; risk assessment using odds ratios and ROC analysis.

    • Main Results:

    • Bacteremia confirmed in 8.2% of episodes (46/558).
    • Higher rates observed in community-acquired (15.6%) versus hospital-acquired (6.0%) episodes.
    • Key predictors included fever, rigors, shock, bladder catheter removal, and specific blood counts; the predictive model outperformed physician judgment.

    Conclusions:

    • Identified clinical predictors and a risk score aid in identifying high-risk geriatric patients for bacteremia.
    • The proposed predictive model shows promise for enhancing early bacteremia recognition and treatment.
    • Further validation is needed to fully evaluate the benefits of the predictive model.