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Patient-centered care involves delivering care beyond inpatient hospitalization. Reflective practice can enhance a patient-centered approach. Reflective practice is a process of reasoning that considers all aspects of the present situation, including practicalities, learning from personal practice, and consideration of patient needs. Patients appreciate care decisions made while considering their input. Involving the patient in their care provides the patient with a sense of contribution rather...
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In the United States, obesity is a prominent concern. It is linked to heightened mortality rates due to increased occurrences of conditions such as hypertension, atherosclerosis, coronary artery disease, and diabetes compared to nonobese individuals. A patient is classified as obese if their actual body weight surpasses the ideal or desirable body weight by 20%, based on Metropolitan Life Insurance Company data. Ideal body weights consider average weights and heights for males and females...
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Pharmacodynamics in Geriatric Patients: Effects of Age01:27

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Age-related pharmacokinetic changes are extensively documented, but understanding age-related pharmacodynamic alterations is relatively limited. This knowledge gap can be partly attributed to the complexity of developing appropriate measures of drug responses compared to bioanalytical methods for determining drug concentrations.Most information regarding age-related differences in human pharmacodynamics originates from cross-sectional studies. However, these studies assume that observed mean...
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Pharmacokinetics in Pediatric Patients: Drug Excretion01:26

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In pediatric medicine, understanding the renal function and drug elimination nuances is crucial for administering safe and effective treatments. Newborns, in particular, display markedly slower renal functions than adults, profoundly affecting how drugs are cleared from their bodies. This slower drug clearance requires clinicians to extend the dosing intervals for many medications to prevent drug accumulation and toxicity while ensuring therapeutic efficacy.One key area where these adjustments...
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Pharmacokinetics in Pediatric Patients: Drug Distribution01:17

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Drug distribution in the pediatric population exhibits unique challenges and considerations due to the physiological differences between children, particularly neonates and infants, and adults. A crucial aspect of pediatric pharmacology is understanding how these differences impact the pharmacokinetics of various drugs, necessitating age-specific dosing strategies to ensure efficacy and safety.Neonates and infants have a higher total body water content, ~75%–90% of their body weight,...
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Updated: Feb 15, 2026

Modified Posterior Vertebral Column Resection for Patients with Thoracolumbar Kyphotic Deformity
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Structure of AI Responses With Complex Patient Analysis for Patient Alternatives and Patient Modifiers.

David C Johnsen1, Leonardo Marchini2, Kim Vo3

  • 1Pediatric Dentistry, College of Dentistry, University of Iowa, Iowa City, Iowa, USA.

European Journal of Dental Education : Official Journal of the Association for Dental Education in Europe
|February 14, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) provides dentists with categorized patient care information, aiding treatment planning without making recommendations. Dentists retain responsibility for patient decisions, privacy, and information credibility.

Keywords:
AI patient alternativesAI structure analysiscritical thinking

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Area of Science:

  • Dental informatics
  • Artificial intelligence in healthcare
  • Clinical decision support

Background:

  • Artificial intelligence (AI) is increasingly utilized in dentistry.
  • Limited data exists on AI's response structure for patient scenarios.

Purpose of the Study:

  • Analyze AI responses for dental treatment planning alternatives.
  • Examine AI's role in patient-specific factors and complex cases.
  • Investigate AI information's impact on dental decision-making, privacy, and credibility.

Main Methods:

  • Utilized Microsoft Copilot with a hypothetical patient scenario.
  • Prompted AI for treatment alternatives and patient analysis factors.
  • Analyzed AI-generated modifying factors, patient capacity, and prognoses.

Main Results:

  • AI provided extensive, categorized, informational (not recommendational) data.
  • AI outlined treatment goals, phases, pros, and cons.
  • AI identified ten modifying factors, seven capacity indicators, and prognoses; treatment alternatives were minimally affected by modifying factors.

Conclusions:

  • AI offers comprehensive patient care information to support dentists.
  • AI does not make treatment recommendations, reinforcing dentist's considerations.
  • Dentists remain accountable for patient decisions, privacy, and AI information validity.