Orthopedic treatment of Class II malocclusion with mandibular deficiency: a clinical practice review
Bibliographic record
Abstract
Abstract: Skeletal Class II patients with concurrent mandibular retrognathia who are still growing, may be treated with removable or fixed functional appliances. The success of treating these patients to an acceptable facial profile and occlusion depends on the amount of remaining growth, the design of the appliance and patient compliance. The objective of this clinical practice review article is to present the literature background of contemporary treatment of Class II malocclusion with mandibular deficiency using a fixed Herbst functional appliance and a removable Invisalign® with mandibular advancement (MA) clear aligner functional appliance. Both of these appliances sequentially advance the mandible into a forward position for Class II correction. This review is illustrated with a clinical case treated with each type of functional appliance, as examples to demonstrate mechanism of action and treatment outcomes. Overall, both appliances deliver similar outcomes in Class II correction with improvement in convexity of facial profile, skeletal changes in decrease in angle A point-nasion-B point (ANB), increase in angle sella-nasion-B point of mandible (SNB), forward movement of the mandible and minor maxillary restraint. They differ in that the Herbst appliance increased proclination of lower incisors, while Invisalign® MA controlled the lower incisor inclination. Invisalign® MA also offered better vertical control. Understanding the mechanisms of action, similarities and differences in treatment outcomes may assist clinicians in selecting the appropriate orthodontic appliance for Class II treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".