Mandibular incisor extraction: a systematic review of an uncommon extraction choice in orthodontic treatment
Bibliographic record
Abstract
OBJECTIVES: To review the published literature in order to address clinical questions regarding the indications, effects and outcomes of mandibular incisor extraction in orthodontics. DATA SOURCES: Three electronic databases were searched: PubMed (1950-January 2011), Ovid Embase+Ovid Embase Classic (1947-January 2011) and Cochrane library (6 Cochrane databases, 1996-January 2011). The following journals were additionally searched: American Journal of Orthodontics and Dentofacial Orthopedics (1960-January 2011), Angle Orthodontist (1960-January 2011), European Journal of Orthodontics (1970-January 2011) and Journal of Orthodontics (1974-January 2011). Grey literature was searched using Google Scholar and System for Information on Grey Literature in Europe. Secondary search of the references cited in the relevant articles was also conducted. DATA SELECTION: Articles in vivo, in Humans, in English, concerning treatment of malocclusion with mandibular incisor extractions or missing mandibular incisors. Fifty-four publications met these inclusion criteria and were reviewed. DATA EXTRACTION: Data were extracted independently by two reviewers with regard to: (1) indications; (2) contraindications; (3) effects; (4) outcomes; and (5) factors associated with successful outcomes. DATA SYNTHESIS: Considering the descriptive nature of the studies found, a narrative synthesis was undertaken. CONCLUSIONS: The descriptive nature of published articles precludes making strong evidence-based recommendations regarding this extraction choice, but it is clear that mandibular incisor extraction can be effectively used in the resolution of crowding, as well as intermaxillary malocclusion in carefully selected cases. Several factors that could lead to good outcomes of orthodontic treatment following mandibular incisor extraction were identified. Mild-to-moderate class III malocclusion, an edge-to-edge anterior occlusion or anterior crossbite, with mild anterior mandibular tooth size excess, and minimal open bite tendencies were the clinical situations most frequently treated with this unique extraction choice. On the other hand, clinicians should be careful to avoid poor outcomes such as gingival recession, open interproximal gingival embrasures, increased overjet and overbite.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".