Comparative accuracy of <scp>cone‐beam CT</scp> and conventional multislice computed tomography for real‐time navigation in zygomatic implant surgery
Bibliographic record
Abstract
BACKGROUND: Cone-beam computed tomography (CBCT) and conventional multislice CT (MSCT) are both used in zygomatic implant navigation surgery but the superiority of one technique versus the other remains unclear. PURPOSE: This study compared the accuracy of CBCT and MSCT in zygomatic implant navigation surgery by calculating the deviations of implants. MATERIAL AND METHODS: Patients with severely atrophic maxillae were classified into two groups according to the use of CBCT- or MSCT-guided navigation system. The entry and apical distance deviation, and the angle deviation of zygomatic implants were measured on fused operation images. A linear effect model was used for analysis, with statistical significance set at P < .05. RESULTS: A total of 72 zygomatic implants were inserted as planned in 23 patients. The comparison of deviations in CBCT and MSCT groups showed a mean (± SD) entry deviation of 1.69 ± 0.59 mm vs 2.04 ± 0.78 mm (P = .146), apical deviation of 2 ± 0.68 mm vs 2.55 ± 0.85 (P < .001), and angle deviation of 2.32 ± 1.02° vs 3.23 ± 1.21° (P = .038). CONCLUSION: Real-time zygomatic implant navigation surgery with CBCT may result in higher values for accuracy than MSCT.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".