A step‐by step guide for autotransplantation of teeth
Bibliographic record
Abstract
Tooth loss is an adverse consequence of oral diseases and traumatic dental injuries. Although several treatment options exist to treat a missing or hopeless tooth, especially in young individuals, most of the existing alternatives (such as orthodontic treatment, removable or fixed partial dentures, and dental implants) impose a challenge in children due to the nature of the developing jaw bones. Tooth autotransplantation is the replacement of a tooth with another functional tooth within the patient's dentition. Autotransplantation can serve as a promising treatment alternative in cases of tooth loss not only in children and adolescents but also in adult patients. Autotransplantation is a technique-sensitive procedure, that requires proper and thorough planning as well as careful and knowledgeable execution in order to improve the chances for long-term success and survival of the transplanted tooth. Thus, the aim of this article was to provide a step-by-step clinical guide, emphasizing key points and highlights for planning and performing a successful autotransplantation procedure. Autotransplantation is a very predictable treatment modality that can serve as a viable option to replace a missing tooth, especially in young patients. Proper planning and careful execution of the procedure are important to achieve optimal long-term results.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".