Failure rates associated with guided versus non-guided dental implant placement: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of the systematic review and meta-analysis was to evaluate implant failure rates and their association with guided and free-hand implant placement techniques. MATERIALS AND METHODS: A literature search was conducted across PubMed, Medline via Ovid, Cochrane database, and Google Scholar. The search was completed in September 2020. Series of meta-analyses were conducted to compare implant failure rates with guided and free-hand techniques. RESULTS: A total of 3387 articles were identified from the electronic search. After applying the inclusion criteria, eight articles were selected for qualitative assessment and four for quantitative synthesis (meta-analysis). The included studies had a risk ratio of 0.29 (95% CI: 0.15, 0.58), P < 0.001 for the use of guided implant placement. Implant failure rates were affected by the different placement techniques indicated by the test for overall effect (Z = 3.53, P = 0.0004). The incidence of implant failure in guided surgery versus free-hand surgery was found to be 2.25% and 6.42%, respectively. CONCLUSION: Both guided and free-hand implant placement techniques resulted in a high implant survival rate. However, implant failure rates were almost three times higher in the free-hand implant placement category. A guided implant placement approach is recommended for a successful outcome.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".