Intraoral Scan Accuracy and Time Efficiency in Implant-Supported Fixed Partial Dentures: A Systematic Review
Bibliographic record
Abstract
The digital implant impression technique (DIT) and conventional implant impression technique (CIT) workflows in implant-supported fixed partial dentures (FPDs) have not been extensively compared in prior studies. Moreover, there is no agreement on the more accurate method that entails less time in the laboratory and during the clinical phases of fabrication and delivery of the prosthesis, respectively. This review aimed to assess the precision of the imaging procedure and overall fabrication time of the DIT and CIT for the implant-supported FPDs. An electronic search was performed using PubMed, Scopus, EMBASE, Cochrane Oral Health Group, and Dentistry and Oral Science Source databases through EBSCO for relevant studies from January 2014 to April 2023. Following the preliminary screening, the studies that met the inclusion criteria underwent full-text review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Cochrane Collaboration risk of bias appraisal tool and Newcastle-Ottawa scale were applied to assess the quality of randomized controlled trials (RCTs) and non-randomized prospective clinical studies, respectively. The initial search yielded 332 studies, and after excluding duplicates, 241 papers were available for screening. Titles and abstracts were reviewed, and 97 articles were chosen for full-text review by two authors independently. Furthermore, 89 articles were excluded in compliance with the PICOS question, and eight studies were chosen for qualitative analysis. Hence, the review comprised two RCTs and six prospective clinical studies. The time efficiency of the implant-supported FPDs was examined in four investigations, three of which used the Trios 3 scanner and one used the Intero scanner. The three-dimensional accuracy of DIT and CIT was compared in six clinical comparative studies. One of the RCTs was rated to have a high risk of bias and the other with a moderate quality of evidence. The six prospective studies were rated to have high-quality of evidence. The findings of this review indicate the prospective applicability of future intraoral scanning systems. The DIT was reported to be outstanding in terms of patient preferences and total fabrication time efficiency. Additional in vivo studies are needed to establish the therapeutic usefulness and time efficiency of integrating DIT in more comprehensive settings.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".