Does a fully digital workflow improve the accuracy of computer‐assisted implant surgery in partially edentulous patients? A systematic review of clinical trials
Bibliographic record
Abstract
BACKGROUND: Accurate implant placement is essential in reducing post-treatment complications and in ensuring a successful treatment outcome. PURPOSE: To compare the accuracy of fully-guided static computer-assisted implant surgery (s-CAIS) using partially- and fully-digital workflows. MATERIALS AND METHODS: Electronic and manual literature searches were performed to collect evidence concerning the accuracy of fully-guided s-CAIS procedures utilizing tooth-supported guides. Quantitative analysis was conducted to evaluate the accuracy of partially- and fully-digital workflows, and survival rates and complications were qualitatively analyzed. RESULTS: Thirteen studies, including 6 randomized controlled trials and 7 prospective clinical studies, were selected for quantitative and qualitative synthesis. A total of 669 implants in 325 patients using s-CAIS were available for review. Meta-analysis of the accuracy revealed a total mean angular deviation of 2.68° (95% CI: 2.32°-3.03°); mean global coronal deviation of 1.03 mm (95% CI: 0.88-1.18 mm); mean global apical deviation of 1.33 mm (95% CI: 1.17-1.50 mm); and mean depth deviation of 0.59 mm (95% CI: 0.46-0.70 mm). Minimal differences were found between the two different workflows. Few complications were reported, and survival rates were between 97.8% to 100% (range of follow-up: 12 to 24 months) in the available studies. CONCLUSION: Similar accuracy is obtained when implants are placed in partially edentulous patients using fully-guided s-CAIS, independently of the workflow utilized.
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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 | 0.024 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".