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Predictability of anterior open bite treatment with Invisalign
Haylea L Blundell1, Tony Weir1, Graeme Byrne1
1La Trobe Rural Health School, La Trobe University, Bendigo, Victoria, Australia.
Introduction:
Anterior open bite correction with Invisalign has been claimed to have relatively good predictability because of the proposed function of clear aligners to function as occlusal bite-blocks, limiting extrusion of the posterior teeth or possibly even intruding posterior teeth. This proposal, however, remains relatively unsubstantiated. The objective of this study was to investigate and determine the accuracy of Invisalign treatment in correcting anterior open bite by comparing the predicted outcome from ClinCheck to the achieved outcome for the initial aligner sequence.
Methods:
A retrospective study used pretreatment and posttreatment intraoral scans and predicted outcomes (ClinCheck) stereolithography files of 76 adult patients from private specialist orthodontic practices. Inclusion criteria comprised nonextraction treatment, with a minimum of 14 dual arch Invisalign aligners. Geomagic Control X software was used to measure overbite and overjet in the pretreatment, posttreatment, and predicted outcomes stereolithography files for each patient.
Results:
Approximately 66.2% of the programmed open bite closure was expressed compared with the prescribed ClinCheck outcome. The use of posterior occlusal bite-blocks and prescribed movement of teeth via anterior extrusion, posterior intrusion, or a combination of the 2 made no difference to the efficacy of open bite closure. Two-week aligner changes resulted in 0.49 mm more bite closure on average.
Conclusions:
The prescribed bite closure in ClinCheck software overestimates the bite closure that is clinically achieved.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.