A minimally invasive method for titanium mesh fixation with resorbable sutures in guided bone regeneration: A retrospective study
Bibliographic record
Abstract
OBJECTIVES: Titanium mesh has become a mainstream choice for guided bone regeneration (GBR) owing to its excellent space maintenance. However, the traditional fixation method using titanium screws impacts surgery efficiency and increases patient trauma. We report a novel method of fixing a titanium mesh using resorbable sutures. We assessed the feasibility of resorbable sutures for fixing a titanium mesh and whether it can serve as a stable, universal, and minimally invasive fixation method for a broader application of titanium meshes. METHODS: Patients undergoing GBR with a digital titanium mesh fixed using titanium screws (TS group) and resorbable sutures (RS group) were observed at different time points. The stability of the fixation methods was evaluated on parameters such as titanium mesh spatial displacement, bone augmentation, and bone resorption. RESULTS: A total of 36 patients were included in this study. The exposure rate of the titanium mesh in the TS group was 16.67%, while no exposure was noted in the RS group. There was no significant difference in the parameters of titanium mesh spatial displacement, bone augmentation, and bone resorption between the two groups (p > 0.05). CONCLUSION: The use of resorbable sutures for fixing a titanium mesh can achieve similar results to traditional fixation using titanium screws. Although this new fixation method can improve the efficiency of the surgery and reduce the risk of complications, the long-term clinical effects require further follow-up investigation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".