Non-Compliance Distalization Appliances Supported by Mini-Implants: A Systematic Review
Bibliographic record
Abstract
Background: A common strategy for the correction of Class II malocclusion is to initially distalize the maxillary molars to create a Class I relationship. Material and Methods: PubMed, Embase, Cochrane Library, Google Scholar, and Clinicaltrials.gov databases were searched to identify and retrieve orthodontic articles that evaluated non-compliance distalization appliances supported by mini-implants up to 11 November 2022. Results: A total of 505 articles were initially identified, and after applying the inclusion criteria, 28 studies were enlisted for evaluation. For the prospective studies, the Risk of bias in non-randomized studies of interventions assessment tool was used, and for the retrospective studies, the Newcastle-Ottawa quality assessment scale. Regarding the palatal devices with mini-implants, the maxillary molars were distalized with a mean value ranging from 2.4 to 5.9 mm, along with a distal tipping ranging between 0.01° and 11°, while when Pendulums were used with mini-implants, the maxillary molars were distalized with a mean value from 1.8 mm to 7.9 mm, and the distal tipping ranged from 7.34° to 22.8°. Further, in the second subgroup, including the appliances placed buccally, the maxillary molars were distalized with a mean value ranging from 1.83 mm to 4.2 mm and a distal tipping ranging between 0.6° and 4.8°. Conclusions: Non-compliance appliances supported by mini-implants are effective in maxillary molar distalization, presenting no anchorage loss of the anterior dental unit in most of the appliances, while distal tipping was found to be more pronounced when the mini-implants were used with Pendulums.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".