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Related Concept Videos

Confirmation Biases01:31

Confirmation Biases

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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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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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Bias01:22

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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.
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In semiconductor devices, diodes play a crucial role in directing current flow, and its operation is primarily categorized into forward bias and reverse bias. A diode is said to be forward-biased when its p-type region is connected to the positive terminal of a battery and its n-type region is linked to the negative terminal. This configuration reduces the potential barrier within the diode, allowing current to flow easily from the p to the n-type region.
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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
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Biasing of P-N Junction01:16

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The operation of a p-n junction diode involves various biasing conditions, including forward bias, reverse bias, and equilibrium.
In equilibrium, no external voltage is applied across the p-n junction. The depletion region is formed at the junction interface due to the diffusion of carriers, which leaves behind charged dopants, acceptors on the p-side, and donors on the n-side. These immobile charges create an electric field that prevents further diffusion of carriers. The related energy band...
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Assessment of Mouse Judgment Bias through an Olfactory Digging Task
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Assessing Racial Bias Within American Society of Anesthesiologists Classification.

Michael A Jacobs1, Brenna N McKaig2, Susanne Schmidt3

  • 1Center for Healthcare Evaluation, Research, and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania.

The Journal of Surgical Research
|January 21, 2026
PubMed
Summary

Racial bias exists in the American Society of Anesthesiologists (ASA) classification system, impacting surgical risk assessment and potentially healthcare disparities. Findings suggest a need to revise ASA class assignment to ensure equitable patient care.

Keywords:
Anesthesiology reimbursementCausal matchingSurgical decision making

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Area of Science:

  • Health Services Research
  • Medical Informatics
  • Health Equity

Background:

  • The American Society of Anesthesiologists (ASA) classification is a standard for surgical risk assessment, patient care decisions, and reimbursement.
  • Existing research has not fully explored race/ethnicity bias within the ASA classification system.
  • Potential healthcare disparities stemming from such bias require investigation.

Purpose of the Study:

  • To investigate potential race/ethnicity bias in the American Society of Anesthesiologists (ASA) classification system.
  • To evaluate differences in ASA class assignment across racial and ethnic groups after controlling for key surgical risk factors.

Main Methods:

  • A retrospective cohort study analyzed 350,187 Veterans Health Administration and 4,051,185 non-Veterans Health Administration surgical cases.
  • Race/ethnicity groups were matched on the Risk Analysis Index (a surgical frailty measure) and preoperative acute serious conditions.
  • Differences in ASA class were assessed using statistical analysis, including adjusted odds ratios and confidence intervals.

Main Results:

  • After matching, Black patients had a higher ASA class compared to White patients (adjusted ORs = 1.37, 1.07).
  • Hispanic patients exhibited a lower ASA class compared to White patients (adjusted ORs = 0.96, 0.96), although this finding showed variability in sensitivity analyses.
  • These disparities indicate a potential bias in ASA class assignment based on race/ethnicity.

Conclusions:

  • The ASA classification system demonstrates variation by race/ethnicity, suggesting bias that may exacerbate healthcare disparities.
  • This bias has practical implications for patient outcomes, preoperative care, surgical delays, costs, and quality improvement initiatives.
  • Revising ASA class assignment, potentially through automated systems with bias safeguards, or adopting alternative morbidity measures may be necessary to ensure equitable care.