Assessment of the outcomes and stability after mandibular incisor extraction in orthodontic patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Background: This study assessed the stability of the outcomes after mandibular incisor extraction (MIE) using intercanine width and peer assessment rating (PAR) scores in orthodontic patients. Methods: PubMed, Cochrane Library, Science Direct, Google Scholar, Ovid, and SciELO were systematically searched without restrictions until August 2022. A risk of bias assessment was performed using Newcastle-Ottawa Scale (NOS). The Grading of Recommendations, Assessment, Development, and Evaluation tool was used to assess the quality of evidence. Random effects meta-analysis was performed using RevMan software. Results: <0.00001). Improvements in PAR scores from the start of treatment to the retention period indicated a high outcome standard (>70%) with MIE treatment, with no significant difference in the reduction percentage compared to premolar and non-extraction groups. Conclusion: With the existing retrospective studies of limited evidence, treatment outcomes with MIE were found to show good improvements in PAR scores. Some reduction in the intercanine width was evident after the retention period, which was observed even with the other two treatment modalities that were compared. Hence, with careful evaluation, MIE could be considered a valid treatment option.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".