The First Alveolar Bone Graft Simulator
Bibliographic record
Abstract
Alveolar bone graft (ABG) surgery in cleft patients is technically challenging. The procedure requires design, dissection and release of soft tissue flaps to create a seal around the bone graft. In addition, visualization during the procedure is challenging within the confines of the cleft. These features make ABG surgery difficult to learn and teach, and it is, therefore, a suitable procedure for the use of a simulator. A high-fidelity cleft ABG simulator was developed using three-dimensional printing, polymer, and adhesive techniques. Simulated ABG surgery was performed by two expert cleft surgeons for a total of five simulation sessions to test the simulator's features and the ability to perform the critical steps of an ABG. ABG surgery was successfully performed on the simulator. The simulations involved interacting with realistic dissection planes as well as multi-layered synthetic soft (periosteum, mucosa, gingiva, adipose tissue) and hard (teeth, bone) tissue. The simulator allowed performance of cleft marginal incisions, dissection, and elevation of a muco-gingival-periosteal flap, creation of nasal upturned and palatal downturned flaps, nasal and palatal side closure, insertion of simulated bone graft material, and advancement of the muco-gingival-periosteal flap for closure of the anterior wall of the cleft. The ABG simulator allowed performance of the critical steps of ABG surgery. This is the first ABG simulator developed, which incorporates the features necessary to practice the procedure from start to finish.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".