Digital workflow for graft harvest and positioning in deficient anterior mandibles versus conventional technique: Randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: The cortical shell technique is frequently associated with technical drawbacks, such as the lack of anatomical guidance during shell harvest and graft. This study aims to assess the horizontal bone gain and accuracy of a digitalized protocol that incorporates two interlocking patient-specific stackable guides (PSSGs) to control the shell harvest, positioning, and fixation. PATIENTS AND METHODS: Twenty patients with deficient anterior mandibles were randomly allocated; 10 patients received freehand symphyseal shell harvest and fixation (the control group), whereas the other 10 received fully guided harvest and graft (study group) using (PSSGs), the first aided an accurate shell harvest, whereas the second conveyed shell fixation. The interposition gap among both groups was loaded with an equal mix of xenogeneic and autogenous particulates. The mean radiographic bone gain among both groups was calibrated 6 months postoperatively, and the accuracy of the digital plan was assessed by superimposing and comparing the virtually planned horizontal bone dimensions with the immediate postoperative actual bone dimensions. RESULTS: The mean 6-month postoperative horizontal bone gain value of the study group was recorded as (4.97 ± 0.73) mm versus (4.45 ± 0.61) mm for the control group, with a statistically insignificant mean gain difference of (0.52) mm, (p = 0.101). The mean virtual preplanned horizontal bone gain was recorded (5.4 ± 0.6) versus (5.4 ± 0.6) for the immediate postoperative actual bone gain, which was also statistically insignificant (p = 0.9). CONCLUSION: The (PSSGs) provided a precise method for graft harvest, position, and fixation, resulting in satisfactory alveolar ridge dimensions with intimate accuracy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".