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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Related Experiment Video

Updated: Jul 2, 2025

High-resolution Melting PCR for Complement Receptor 1 Length Polymorphism Genotyping: An Innovative Tool for Alzheimer's Disease Gene Susceptibility Assessment
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ADCOMS sensitivity versus baseline diagnosis and progression phenotypes.

Dave Evenden1, Angus Prosser1, Sofia Michopoulou1,2

  • 1Clinical and Experimental Sciences University of Southampton Southampton UK.

Alzheimer'S & Dementia (Amsterdam, Netherlands)
|February 26, 2024
PubMed
Summary
This summary is machine-generated.

The Alzheimer's Disease COMposite Score (ADCOMS) shows greater sensitivity in detecting decline in clinical trials. Clustering ADCOMS trajectories reveals distinct patterns of disease progression.

Keywords:
ADCOMSAlzheimer's Diseaseclusteringlongitudinal change

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Area of Science:

  • Neuroscience
  • Clinical Trials
  • Biostatistics

Background:

  • The Alzheimer's Disease COMposite Score (ADCOMS) is a sensitive measure for detecting decline in pre-dementia stages.
  • Conventional measures may lack sensitivity in early Alzheimer's disease (AD) clinical trials.

Purpose of the Study:

  • To compare ADCOMS trajectories using clustered progression characteristics.
  • To identify distinct patterns of decline in individuals with pre-dementia.

Main Methods:

  • Analysis of post-baseline ADCOMS values using the mean-to-standard deviation ratio (MSDR).
  • Clustering individuals into similar ADCOMS progression trajectories.
  • Comparison of MSDR across different progression groups and baseline diagnoses.

Main Results:

  • ADCOMS demonstrated increased sensitivity for clinically important progression groups.
  • ADCOMS showed limited sensitivity for less-severe baseline diagnoses and marginal progression groups.

Conclusions:

  • This study validates and extends the known sensitivity of ADCOMS.
  • Clustering ADCOMS progression offers a novel approach to evaluating disease trajectories in large datasets.