Determinants for success rates of temporary anchorage devices in orthodontics: a meta-analysis (n > 50)
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to review the literature and evaluate the failure rates and factors that affect the stability and success of temporary anchorage devices (TADs) used as orthodontic anchorage. METHODS: Data were collected from electronic databases: MEDLINE database, Scopus, and Web of Knowledge. Four combinations of term were used as keywords: screw orthodontic failure, screw orthodontic success, implant orthodontic failure, and implant orthodontic success. The following selection criteria were used to select appropriate articles: articles on implants and screws used as orthodontic anchorage, data only from human subjects, studies published in English, studies with more than 50 implants/screws, and both prospective and retrospective clinical studies. RESULTS: The search provided 209 abstracts about TADs used as anchorage. After reading and applying the selection criteria, 26 articles were included in the study. The data obtained were divided into two topics: which factors affected TAD success and to what degree and in how many articles they were quoted. Clinical factors were divided into three main groups: patient-related, implant-related, and management-related factors. CONCLUSIONS: Although all articles included in this meta-analysis reported success rates of greater than 80 per cent, the factors determining success rates were inconsistent between the studies analysed and this made conclusions difficult.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".