Autotransplantation of mesiodens for missing maxillary lateral incisor with cone‐beam <scp>CT</scp>‐fabricated model and orthodontics
Bibliographic record
Abstract
AIM: Autotransplantation is a viable treatment option for a missing tooth when there is a suitable donor, especially in adolescents with remaining facial growth. This report presents the aesthetic restoration of a missing maxillary lateral incisor through orthodontic treatment and autotransplantation of a mesiodens using a CBCT-fabricated rapid-prototyping model. SUMMARY: A 14-year-old male patient with a congenitally missing maxillary lateral incisor was referred from the Department of Orthodontics. The teeth were moved orthodontically to regain space for the missing lateral incisor and to close the space of the mesiodens after transplantation. A replica of the donor tooth was fabricated from a cone-beam computed tomography scan through a rapid-prototyping machine before autotransplantation surgery. The model was used to create a socket for the graft tooth, thereby shortening the extra-oral time and minimizing the damage to the root surface. After transplantation and orthodontic tooth movement, the mesiodens was finally restored with an aesthetic laminate restoration. Over 3 years, the aesthetics remained excellent, and the transplant functioned normally without any signs or symptoms of root resorption. KEY LEARNING POINT: Missing anterior teeth may be replaced through a combination of orthodontics, autotransplantation with a rapid-prototyping model and prosthodontic restoration, in growing patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".