Rapid maxillary expansion effects in Class II malocclusion: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of rapid maxillary expansion (RME) on the sagittal dental or skeletal parameters of growing children with Class II malocclusion. MATERIALS AND METHODS: A systematic review intended to identify relevant literature was conducted. The search was performed on Medline, Embase, Cochrane Library, and Scopus databases. Reference lists of the included articles were also screened for relevant documents. The qualitative assessment was performed according to the Methodological Index for Non-Randomized Studies (MINORS) tool, and the resultant data were grouped and analyzed concerning dental and skeletal sagittal effects of RME. RESULTS: Of 25 screened studies, seven articles met eligibility criteria and were included. Study samples were observed during mixed dentition stage and characterized as having either Class II dental malocclusion or skeletal discrepancy. None of the included studies was a randomized clinical trial. Included controlled studies presented several inadequacies related to control group or lacked appropriate comparative statistical analysis. Besides being frequently based on deficient methodology, dental and skeletal sagittal effects of RME were either controversial or lacked clinical relevance. CONCLUSION: The effect of RME on the sagittal dimension of Class II malocclusions has not been proved yet. Future randomized controlled clinical trials are still needed to definitely address this question.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".