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The Establishment of a Murine Maxillary Orthodontic Model
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League tables for orthodontists.

Frank Dunstan1, Stephen Richmond, Ceri Phillips

  • 1Department of Primary Care and Public Health, Cardiff University, UK. dunstanfd@cardiff.ac.uk

European Journal of Orthodontics
|August 9, 2008
PubMed
Summary

Constructing orthodontic league tables requires accounting for patient case mix to accurately rank clinician success. Bayesian modeling helps quantify uncertainty, ensuring reliable comparisons for informed decision-making.

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Area of Science:

  • Dental Public Health
  • Health Services Research
  • Orthodontic Outcomes

Background:

  • League tables are increasingly used to compare healthcare providers, including orthodontists.
  • Accurate measurement of orthodontic clinical outcomes is complex and requires careful consideration of various factors.
  • Existing methods for ranking orthodontists may not adequately account for patient complexity and treatment variability.

Purpose of the Study:

  • To explore the complexities involved in creating league tables for measuring orthodontic clinical outcomes.
  • To assess the impact of patient case mix on the accuracy of orthodontic performance rankings.
  • To quantify the uncertainty associated with success rates and rankings of orthodontists.

Main Methods:

  • Utilized the Index of Complexity, Outcome, and Need (ICON) to assess patient need, complexity, and treatment outcomes.
  • Collected data from 18 orthodontists, each providing information on 100 consecutively treated patients.
  • Employed Bayesian hierarchical modeling to account for case mix and quantify uncertainty in rankings.

Main Results:

  • Overall successful orthodontic outcomes (ICON score ≤ 30) were achieved in 62% of patients, with significant variation (19-94%) among orthodontists.
  • Four of the 18 orthodontists had success rates below 50%.
  • After accounting for case mix, significant differences in performance between lower and higher-ranked orthodontists became evident.

Conclusions:

  • Accurate construction of orthodontic league tables necessitates the identification and control of confounding factors like patient case mix.
  • Bayesian hierarchical modeling provides a robust method for creating reliable league tables and quantifying uncertainty.
  • Well-constructed league tables can empower informed choices for patients, providers, and payers in orthodontic care.

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