Article Bias: The article presents a data-driven approach to predicting parameters related to infant skull fractures to aid in distinguishing between abusive head trauma and accidental injuries, focusing on the application of machine learning technology in healthcare. It appears to be objective in its presentation of methods and results, highlighting the limitations of the study while advocating for further advancements in the field.
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ðĩ Liberal <â> Conservative ðī:
ð― Libertarian <â> Authoritarian ð:
ðïļ Objective <â> Subjective ðïļ :
ðĻ Sensational:
ð Bearish <â> Bullish ð:
ð Prescriptive:
ðïļ Dovish <â> Hawkish ðĶ:
ðĻ Fearful:
ð Begging the Question:
ðĢïļ Gossip:
ð Circular Reasoning:
ð Covering Responses:
ðĒ Victimization:
ðĪ Overconfident:
ðïļ Spam:
â Ideological:
ðī Anti-establishment <â> Pro-establishment ðš:
ð Negative <â> Positive ð:
ðð Double Standard:
â Uncredible <â> Credible â :
ð§ Rational <â> Irrational ðĪŠ:
ðĪ Advertising:
ðŽ Scientific <â> Superstitious ðŪ:
ðĪ Written by AI:
ð Low Integrity <â> High Integrity âĪïļ:
AI Bias: Neutral on science/tech, structured approach to analyzing bias.
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