Reporting characteristics of meta-analyses in orthodontics: methodological assessment and statistical recommendations
Bibliographic record
Abstract
Ideally meta-analyses (MAs) should consolidate the characteristics of orthodontic research in order to produce an evidence-based answer. However severe flaws are frequently observed in most of them. The aim of this study was to evaluate the statistical methods, the methodology, and the quality characteristics of orthodontic MAs and to assess their reporting quality during the last years. Electronic databases were searched for MAs (with or without a proper systematic review) in the field of orthodontics, indexed up to 2011. The AMSTAR tool was used for quality assessment of the included articles. Data were analyzed with Student's t-test, one-way ANOVA, and generalized linear modelling. Risk ratios with 95% confidence intervals were calculated to represent changes during the years in reporting of key items associated with quality. A total of 80 MAs with 1086 primary studies were included in this evaluation. Using the AMSTAR tool, 25 (27.3%) of the MAs were found to be of low quality, 37 (46.3%) of medium quality, and 18 (22.5%) of high quality. Specific characteristics like explicit protocol definition, extensive searches, and quality assessment of included trials were associated with a higher AMSTAR score. Model selection and dealing with heterogeneity or publication bias were often problematic in the identified reviews. The number of published orthodontic MAs is constantly increasing, while their overall quality is considered to range from low to medium. Although the number of MAs of medium and high level seems lately to rise, several other aspects need improvement to increase their overall quality.
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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 | 0.585 | 0.845 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.024 | 0.055 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.010 | 0.006 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".