Prognostic Evaluation
Prognostic Evaluation - Related Videos
08:08
Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
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Cited by 8 •
2013
A structured protocol is presented for a cell-based assay as a functional test to predict the prognosis of idiopathic scoliosis using cellular dielectric spectroscopy (CDS). The assay can be performed with fresh or frozen peripheral blood mononuclear cells (PBMCs) and the procedure is completed within 4 days.
06:03
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
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Cited by 41 •
2020
Breast cancer related lymphedema is frequent in breast cancer survivors, but there are not widely employed guidelines for its diagnosis and quantification. Here, we introduce a reliable and cost-effective protocol to define, quantify, and compare upper limb volume in breast cancer patients.
08:04
Evaluating the Accuracy of Snap Judgments
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2023
Source: Diego Reinero & Jay Van Bavel—New York University Social psychologists have long been interested in the way people form impressions of others. Much of this work has focused on the errors people make in judging others, such as the exaggerated influence of central traits (such as "warm" and "cold"), the insufficient weight given to the context in which others' behavior takes place, and the tendency for people to make judgments that conform to their initial expectations about another.
08:28
Methods for ECG Evaluation of Indicators of Cardiac Risk, and Susceptibility to Aconitine-induced Arrhythmias in Rats Following Status Epilepticus
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Cited by 3 •
2011
Techniques for measurement of electrical activity of the heart by electrocardiogram (ECG), and analysis of cardiac risk factors and susceptibility to arrhythmias following status epilepticus (SE) in the rat are described.
03:00
Self-Evaluation: Self-Enhancement and Self-Verification
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2020
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
FAQs
Discrimination indicates how effectively a model separates people with different future outcomes, whereas calibration examines whether predicted risks correspond to what is actually observed. A model may distinguish higher- and lower-risk individuals yet systematically overestimate or underestimate their risks. Considering both properties gives a more complete statistical assessment and prevents reliance on a single performance perspective.
Time-to-event analysis incorporates when an outcome occurs rather than considering only whether it occurred. This is especially relevant when prognosis concerns survival or disease progression, because individuals may experience outcomes at different times. Using this perspective allows evaluation to reflect the timing of predicted and observed outcomes, supporting more informative interpretation of longitudinal biomedical data.
The biomarker should be assessed alongside established clinical factors rather than evaluated in isolation. Prognostic evaluation can then examine whether its inclusion improves the model's ability to distinguish risks or align predicted risks with observed outcomes. This comparison clarifies whether the candidate predictor contributes useful information for risk stratification or merely duplicates what existing factors already provide.
Validation tests whether observed performance remains reliable beyond the data used to develop or assess the model. When results are examined in new data, investigators can identify whether apparent predictive ability is consistent or may reflect limitations of the original dataset. This step strengthens interpretation by addressing uncertainty and highlighting potential bias before broader application.
A practical evaluation begins by comparing predicted risks with observed outcomes, then selecting performance measures suited to the prognostic question. Investigators assess discrimination, calibration, and, when outcomes unfold over time, time-to-event behavior. They subsequently examine performance in new data and interpret uncertainty, bias, and limitations before deciding whether the findings support further use.
Its results can guide risk stratification by indicating how confidently individuals may be assigned to different prognostic groups. The findings also inform study design and help judge whether a candidate model or biomarker offers evidence relevant to treatment response, disease progression, or survival. Careful interpretation is essential because uncertainty, bias, and performance limitations affect how results should be applied.