Multidisciplinary Approach for Autotransplantation and Restoration of a Maxillary Premolar into an Area of an Avulsed Anterior Tooth: A Case Report with a 6-Year Follow-up
Bibliographic record
Abstract
The loss of an anterior tooth because of avulsion has been reported in up to 3% of dental injuries. Management alternatives, such as implant-supported restorations and a fixed partial denture, are contraindicated in growing patients because of the continuous growth of the alveolar process. At the same time, orthodontic treatment for gap closure will result in asymmetry and will require adjustment of the adjacent healthy teeth. Therefore, restoring a missing tooth imposes a treatment challenge, especially in children and young adults. Tooth autotransplantation is a treatment modality with high reported survival and success rates that overcome these mentioned limitations. It might also help to preserve the alveolar bone and the soft tissues. This case report describes a multidisciplinary approach for managing a 13-year-old boy who lost his right maxillary central incisor because of a fall. Management included autotransplantation of the left maxillary second premolar to the site of the lost right maxillary central incisor, management of external inflammatory resorption with an endodontic treatment, orthodontic treatment, and aesthetic restoration of the area with composite resin crowns that can be adapted to the expected changes of the jaws during the craniofacial growing period. At the 6-year follow-up, the teeth demonstrated a positive outcome.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".