Dynamic navigation for prosthetically driven zygomatic implant placement in extensive maxillary defects: Results of a prospective case series
Bibliographic record
Abstract
BACKGROUND: Zygomatic implants (ZIs) that are anchored in remote locations can significantly improve the retention and stability of prosthetic obturation in maxillary defect sites without the need for complex surgical reconstruction. However, ZI placement without alveolar bone guidance is challenging, especially when identifying the ideal three-dimensional location of the "coronal part" of the implant fixture for further rehabilitation. PURPOSE: To investigate the feasibility and accuracy of surgical navigation for ZI placement using prosthetically driven plans to treat extensive maxillary defects. MATERIAL AND METHODS: Software was used to virtually plan the number and distribution of ZIs for maxillary defect patients according to the existing residual maxillofacial bone. The "coronal part" of the ZI was identified based on the existing maxillodental prosthetic flange, virtually planned infrastructure framework and superstructure. ZI placement was fully guided by surgical navigation. RESULTS: Ten patients were enrolled. One patient had loose reflective spheres, which resulted in an inaccurate back-reflection of the position during navigation. A total of 27 ZIs in the remaining 9 patients were placed uneventfully. The mean of the "coronal part" of the ZI, entry point of the zygomatic bone, apical deviation, and apical angle deviation were 1.50 mm, 1.39 mm, 1.95 mm, and 2.32°, respectively. A Computer-aided design and computer-aided manufacture milled bar and additional attachments were fabricated according to preoperative planning. No osseointegrated implant loss was detected. Six ZIs in three patients received implant surface polishing to reduce soft tissue irritation, and one ZI was submerged. DISCUSSION: Surgical navigation facilitated the accurate and feasible placement of prosthetically driven ZIs that were preoperatively planned in patients with extensive maxillary defects.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".