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Related Concept Videos

Bias01:22

Bias

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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Hindsight Biases01:12

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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? 
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The Representativeness Heuristic

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Related Experiment Video

Updated: May 21, 2025

Assessment of Mouse Judgment Bias through an Olfactory Digging Task
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Inherent Bias in ROSA® Zimmer Biomet Pre-Op Planning Using 2D to 3D X-Atlas® Coronal Knee Axis Measurement.

Michał A Duchniewicz1,2, Aly Shaaban2, Manuel Müller1

  • 1Department of Orthopaedic Surgery, University of Saarland, 66421 Homburg, Germany.

Journal of Clinical Medicine
|March 17, 2025
PubMed
Summary

The ROSA® Knee System accurately predicts component sizes for total knee replacement. While it shows a slight varus bias in initial knee axis planning, final outcomes remain within acceptable surgical standards.

Keywords:
2D to 3D X-Atlas®ROSA® Zimmer Biometorthopaedic surgeryrobotic knee replacementtotal knee replacement

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Area of Science:

  • Orthopedic Surgery
  • Robotics in Medicine
  • Biomedical Engineering

Background:

  • Robotic assistance is explored to enhance precision in total knee replacement (TKR).
  • The ROSA® Knee System (Zimmer Biomet) utilizes 2D to 3D X-Atlas® for pre-procedural planning.
  • Assessing inherent biases and accuracy of robotic planning is crucial for TKR outcomes.

Purpose of the Study:

  • To evaluate the accuracy of pre-procedural planning using the ROSA® Knee System.
  • To compare robotic system measurements with senior consultant assessments.
  • To analyze the final knee axis outcomes in robotic-assisted TKR.

Main Methods:

  • A cohort of 55 patients undergoing robotic-assisted TKR with the ROSA® system was studied.
  • Pre-procedural measurements from ROSA® were compared against senior consultant data.
  • Implanted component sizes were compared with ROSA® predictions.
  • Final coronal knee axis measurements were recorded during surgery.

Main Results:

  • Femur component size prediction accuracy was high (98.2% exact or accurate).
  • Tibial component size prediction showed 92.7% exact or accurate matches.
  • ROSA® planning demonstrated a statistically significant 0.83° varus bias in knee axis assessment (p=0.001).
  • Final coronal knee axis averaged 0.37° varus, within the standard ±3° range.

Conclusions:

  • The ROSA® Knee System demonstrates high accuracy in predicting implanted component sizes for TKR.
  • A statistically significant, though small, varus bias exists in the system's initial knee axis assessment.
  • Despite the bias, the ROSA® system achieves final knee axis alignment within accepted orthopedic standards.