Accuracy and Complications Using Computer‐Designed Stereolithographic Surgical Guides for Oral Rehabilitation by Means of Dental Implants: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: In the last decade several stereolithographic guided surgery systems were introduced to the market. In this context, scientific information regarding accuracy of implant placement and surgical and prosthodontical complications is highly relevant as it provides evidence to implement this surgical technique in a clinical setting. PURPOSE: To review data on accuracy and surgical and prosthodontical complications using stereolithographical surgical guides for implant rehabilitation. MATERIAL AND METHODS: PubMed database was searched using the following keywords: "three dimensional imaging,""image based surgery,""flapless guided surgery,""customized drill guides,""computer assisted surgery,""surgical template," and "stereolithography." Only papers in English were selected. Additional references found through reading of selected papers completed the list. RESULTS: In total 31 papers were selected. Ten reported deviations between the preoperative implant planning and the postoperative implant locations. One in vitro study reported a mean apical deviation of 1.0 mm, three ex vivo studies a mean apical deviation ranging between 0.6 and 1.2 mm. In six in vivo studies an apical deviation between 0.95 and 4.5 mm was found. Six papers reported on complications mounting to 42% of the cases when stereolithographic guided surgery was combined with immediate loading. CONCLUSION: Substantial deviations in three-dimensional directions are found between virtual planning and actually obtained implant position. This finding and additionally reported postsurgical complications leads to the conclusion that care should be taken whenever applying this technique on a routine basis.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".