Prognostic factors associated with the success rates of posterior orthodontic miniscrew implants
Bibliographic record
Abstract
Objective: To systematically review previous studies and to assess, via a subgroup meta-analysis, the combined odds ratio (OR) of prognostic factors affecting the success of miniscrew implants (MIs) inserted into the buccal posterior region. Methods: Three electronic searches that were limited to articles on clinical human studies using MIs that were published in English prior to March 2015 were conducted. The outcome measure was the success of MIs. Patient factors included age, sex, and jaw of insertion (maxilla vs. mandible), while the MI factors included length and diameter. A meta-analysis was performed on 17 individual studies. The quality of each study was assessed for non-randomized studies and quantified using the Newcastle-Ottawa Scale. The meta-analysis outcome was a combined OR. Subgroup and sensitivity analyses based on the study design, study quality, and sample size of miniscrews implanted were performed. Results: Significantly higher success rates were revealed for MIs inserted in the maxilla, for patients ≥ 20 years of age, and for long MIs (≥ 8 mm) and MIs with a large diameter (> 1.4 mm). All subgroups acquired homogeneity, and the combined OR of the prospective studies (OR, 3.67; 95% confidence interval [CI], 2.10−6.44) was significantly higher in the maxilla than that in the retrospective studies (OR, 2.10; 95% CI, 1.60−2.74). Conclusions: When a treatment plan is made, these risk factors, i.e. jaw of insertion, age, MI length, and MI diameter, should be taken into account, while sex is not critical to the success of MIs.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".