Accuracy of a real‐time surgical navigation system for the placement of quad zygomatic implants in the severe atrophic maxilla: A pilot clinical study
Bibliographic record
Abstract
BACKGROUND: A real-time surgical navigation system potentially increases the accuracy when used for quad-zygomatic implant placement. PURPOSE: To evaluate the accuracy of a real-time surgical navigation system when used for quad zygomatic implant placement. MATERIALS AND METHODS: Patients with severely atrophic maxillae were prospectively recruited. Four trajectories for implants were planned, and zygomatic implants were placed using a real-time surgical navigation system. The planned-placed distance deviations at entry (entry deviation）points, exit (exit deviation) points, and angle deviation of axes (angle deviation) were measured on fused operation images. The differences of all the deviations between different groups, classified based on the lengths and locations of implants, were analysed. A P value of < 0.05 indicated statistical significance. RESULTS: Forty zygomatic implants were placed as planned in 10 patients. The entry deviation, exit deviation and angle deviation were 1.35 ± 0.75 mm, 2.15 mm ± 0.95 mm, and 2.05 ± 1.02 degrees, respectively. The differences of all deviations were not significant, irrespective of the lengths (P = .259, .158, and .914, respectively) or locations of the placed implants (P = .698, .072, and .602, respectively). CONCLUSION: A real-time surgical navigation system used for the placement of quad zygomatic implants demonstrated a high level of accuracy with only minimal planned-placed deviations, irrespective of the lengths or locations of the implants.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".