Model Evaluation
Model Evaluation - Related Videos
07:34
Use of Animal Model of Sepsis to Evaluate Novel Herbal Therapies
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Cited by 13 •
2012
Sepsis refers to a systemic inflammatory response syndrome resulting from a microbial infection, and can be simulated by a surgical technique termed cecal ligation and puncture (CLP). Here we describe a method to use CLP-induced animal model to screen medicinal herbs for therapeutic agents.
01:29
Self-Evaluation Maintenance Model
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2025
The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
04:53
Zebrafish Larvae as a Model to Evaluate Potential Radiosensitizers or Protectors
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2022
The zebrafish has recently been exploited as a model to validate potential radiation modifiers. The present protocol describes the detailed steps to use zebrafish embryos for radiation-based screening experiments and some observational approaches to evaluate the effect of different treatments and radiation.
05:23
Establishment and Evaluation of a Sheep Model of Full-thickness Osteochondral Defect
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2026
Here, we present an integrated protocol for generating controlled osteochondral lesions in sheep knees to establish an experimental model suitable for bone and cartilage tissue regeneration studies. The protocol outlines a method for inducing a full-thickness osteochondral defect in the femoral condyle, utilizing techniques that minimize animal welfare impacts and variability in results.
07:33
A Simple Composite Phenotype Scoring System for Evaluating Mouse Models of Cerebellar Ataxia
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Cited by 415 •
2010
We describe a protocol for the rapid and sensitive quantification of disease severity in mouse models of cerebellar ataxia. Measures include hind limb clasping, ledge test, gait and kyphosis. This protocol effectively discriminates between affected and non-affected individuals, and detects the progression of affected individuals over time.
FAQs
Keeping training and test data separate allows evaluation to focus on observations not used to develop the model. This provides evidence about performance on data relevant to the intended purpose rather than only performance during development. In medical research, that distinction helps determine whether predictions are dependable enough to support further investigation or clinical decision-making.
Discrimination and calibration answer different questions. Discrimination concerns how well predictions distinguish among observed clinical outcomes, whereas calibration concerns whether predicted results correspond appropriately to what is observed. A model may therefore require examination from both perspectives, rather than relying on one summary measure, to judge its usefulness for diagnostic, prognostic, or treatment-support purposes.
Performance should be examined across disease prevalences because a model’s apparent usefulness may depend on the clinical population in which it is assessed. Comparing results across prevalence levels can reveal limits to generalizability and show whether conclusions from one setting transfer to another. This is especially important when evidence is intended for varied medical settings or patient groups.
Assessment across patient groups and clinical settings can detect uneven performance and possible bias. Rather than treating one overall result as sufficient, evaluators compare how the model behaves in different populations and environments. These comparisons help identify where evidence is limited and prevent broad claims of reliability when performance has only been examined in a narrow context.
A practical evaluation workflow begins by linking the model to its intended clinical purpose, then comparing predictions with observed outcomes using separate training and test data. Cross-validation can add another assessment during development. Evaluators then examine discrimination, calibration, sensitivity, specificity, and prediction error, followed by checks across patient groups, settings, and disease prevalences to assess generalizability.
Model evaluation supports several medical uses, including diagnostic models, prognostic models, and tools that support treatment decisions. The relevant question is not simply whether a model produces predictions, but whether those predictions show sufficient accuracy, reliability, and usefulness for the stated purpose. Results therefore inform whether a model offers dependable evidence for research or clinical decision-making.
Prediction error adds information that individual performance measures may not capture. Examining error alongside sensitivity and specificity, while also considering calibration and discrimination, gives a broader view of how predictions compare with observed outcomes. This combination helps investigators characterize strengths and limitations before using a model as evidence in medicine, where performance must remain relevant to its intended application.