A systematic review of the accuracy of digital surgical guides for dental implantation
Bibliographic record
Abstract
PURPOSE: This review aimed to reveal the influence of implant guides on surgical accuracy with regard to supporting types, manufacturing methods and design (including fixation screws and sleeves). METHODS: A literature search related to accuracy of surgical guides for dental implantation was performed in Web of Science and PubMed. Studies with in vivo or in vitro deviation data published in recent 5 years (2018-2022) were included and assessed by Newcastle-Ottawa Scale with regard to risk of bias and reliability degree of clinical studies. Accuracy-related deviation data were summarized as forest plots and normal distributions. RESULTS: Forty-one articles were included with high degree of credibility. Data showed that implant surgery accuracy can be achieved with mean distance deviation < 2 mm (most < 1 mm) and angular deviation < 8° (most < 5°). CONCLUSIONS: Bilateral tooth-supported guides exhibited highest in vitro accuracy and similar in vivo accuracy to unilateral tooth-supported guides; mucosa-supported guides exhibit lowest in vivo accuracy, while its in vitro data showed low credibility due to mechanical complexity of living mucosa tissue. Milling exhibited higher in vivo accuracy of guides than 3d-printing, though further data support was needed. Design of fixation screws and sleeves of implant guides affected the surgical accuracy and might remain a research focus in near future. However, lack of universal evaluation standards for implantation accuracy remained a major problem in this field. The influence of implant guides on surgical accuracy revealed in this review might shed light on future development of dental implantology.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".