A mapping review of systematic reviews in orthodontics: a five-year analysis
Bibliographic record
Abstract
OBJECTIVES: This mapping review aimed to identify trends, frequently reviewed topics and assess the methodological quality of recent orthodontic systematic reviews (SRs). METHODS: SRs published between January 2018 and June 2023 were retrieved from PubMed, EMBASE, SCOPUS, Web of Science, and Google Scholar. Two reviewers independently selected studies and extracted data, with a third resolving discrepancies. Methodological quality was evaluated using AMSTAR-2. RESULTS: From 3,131 initial articles, 430 SRs were included. A publication increase of over 50% occurred from 2019 to 2022. The most frequent topics were palatal expansion (12.6%), techniques to accelerate orthodontic movement (11.6%), and clear aligners (9.3%). Only 18.2% of SRs were rated as high or moderate quality, with those on clear aligners rated the lowest (4.9%). Common methodological weaknesses included a lack of protocol registration, absence of excluded study lists, and failure to address publication bias. CONCLUSIONS: Orthodontic SRs have increased significantly over the five-year period assessed, with notable increase in contributions from specific countries. However, most SRs exhibited low methodological quality, raising concerns about clinical applicability. Improved adherence to methodological and reporting standards is crucial for enhancing SR quality and credibility.
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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 | 0.126 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.110 | 0.082 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".