Assessment of bone gain and neurosensory affection with the sandwich osteotomy technique for vertically deficient posterior mandible using a full digital workflow versus conventional protocol: A randomized split mouth study
Bibliographic record
Abstract
BACKGROUND: Using the sandwich osteotomy technique in the posterior mandible is delicate. This study aimed to assess the safety and the amount of bone gain using a full digital workflow versus the conventional procedure. PATIENTS AND METHODS: This split mouth study included 10 patients with bilateral vertically deficient posterior mandible. One side received conventional sandwich interpositional bone grafting (control group), while the other side received the same protocol using two patient-specific guides. The first guide (cutting guide) was used to place the osteotomies safely and accurately according to the predetermined dimensions and locations, and the second guide was used to fix the mobilized bony segment, leaving the desired gap to be filled with a particulate xenogenic bone graft. RESULTS: Full neurosensory recovery was documented at 2 months postoperative for all patients and bilaterally. After 4 months, there was a statistically significant difference in vertical bone gain between both groups (p = 0.001), measuring an average of 3.76 ± 0.72 mm in the study group and 2.69 ± 0.37 mm in the control group. No statistically significant difference was found between the planned vertical augmentation (3.85 ± 0.58 mm) and the obtained vertical bone gain (3.76 ± 0.72 mm) in the study group (p = 0.765) proving the accuracy of the guided procedure. CONCLUSION: Computer-guided sandwich interpositional grafting is predictable regarding the execution of the osteotomies and the accuracy of fixation of the transport segment.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".