Outcomes of Using 3D-Printed Titanium Implants in Mandibular Reconstruction
Bibliographic record
Abstract
A BSTRACT Background: Mandibular reconstruction is a critical procedure in the management of defects caused by trauma, tumors, or congenital anomalies. Traditional methods often face challenges such as poor fit, extended surgery time, and postoperative complications. The advent of 3D printing technology allows for the precise fabrication of titanium implants tailored to individual anatomy. This study evaluates the clinical outcomes of using 3D-printed titanium implants in mandibular reconstruction. Materials and Methods: This prospective study involved 30 patients (mean age: 45 ± 12 years) with mandibular defects requiring reconstruction. Customized 3D-printed titanium implants were designed using preoperative CT scans and fabricated using laser powder bed fusion technology. Surgical procedures included implant placement combined with soft tissue grafts where necessary. Follow-ups were conducted at 1, 3, 6, and 12 months postoperatively to assess implant stability, functional outcomes (mastication and speech), and aesthetic satisfaction. Data were analyzed using descriptive statistics and paired t -tests for functional improvement. Results: All patients successfully underwent implantation without intraoperative complications. At 12 months, the implant survival rate was 96.7%. Functional assessment showed a significant improvement in chewing efficiency (mean increase: 40%, P < 0.001) and speech clarity (mean increase: 30%, P < 0.001). Patient satisfaction regarding aesthetics was high, with 85% rating outcomes as excellent. Postoperative complications included mild infection in two patients (6.7%) and implant loosening in one patient (3.3%), all managed successfully. Conclusion: 3D-printed titanium implants demonstrated excellent clinical efficacy and safety in mandibular reconstruction, offering significant functional and aesthetic benefits. The technology holds promise for personalized care in maxillofacial surgery, though long-term studies are needed to confirm these findings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".