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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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. 
The...
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

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,...
Steps in the Modeling Process01:14

Steps in the Modeling Process

Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
Hindsight Biases01:12

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

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Related Experiment Video

Updated: May 16, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

[Predictive models for ART].

P Arvis1, A Guivarc'h-Levêque, E Varlan

  • 1Clinique La Sagesse, Place Saint-Guenole, 35000 Rennes, France. dr-arvis@wanadoo.fr

Journal De Gynecologie, Obstetrique Et Biologie De La Reproduction
|November 28, 2012
PubMed
Summary
This summary is machine-generated.

Predictive models estimate pregnancy probability but often yield poor results. Adjusting existing models to specific ART center data offers the best current solution for improving practice.

Related Experiment Videos

Last Updated: May 16, 2026

Visualizing Visual Adaptation
04:43

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

  • Reproductive Medicine
  • Biostatistics
  • Clinical Prediction Models

Context:

  • Developing accurate predictive models for pregnancy is crucial in assisted reproductive technologies (ART).
  • Existing models for spontaneous pregnancies, intrauterine insemination (IUI), and in vitro fertilization (IVF) have shown limited success and poor external validation.
  • The impact of these models on improving medical practice has been rarely assessed.

Purpose:

  • To review the development, validation, and performance assessment of predictive models for pregnancy.
  • To highlight the limitations of current models and propose criteria for an ideal ART predictive model.
  • To suggest practical approaches for optimizing the use of predictive models in clinical settings.

Summary:

  • Predictive model development involves formulation, internal/external validation, and impact studies, assessed by discrimination and calibration.
  • Numerous ART predictive models exist but often have poor results, with infrequent and inconclusive external validation.
  • The ideal model is center-specific, aiding treatment choices (abstention, IUI, IVF) and enabling objective center performance comparisons.

Impact:

  • Current predictive models have not significantly improved ART medical practices.
  • An ideal center-specific model could rationalize treatments, preventing premature, late, or futile interventions.
  • Adjusting validated models to center-specific data and performance offers the most effective current strategy for improving clinical decision-making.