Does intra‐oral scan improve the impression accuracy of full‐arch implant‐supported prostheses: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objectives The present study aimed to systematically review the studies comparing the accuracy of intraoral scan (IOS) and conventional implant impressions (CI) in completely edentulous patients. Materials and Methods Electronic searches were performed in PubMed, Embase and Cochrane CENTRAL up to December 1, 2023. Clinical studies and in vitro studies reporting the accuracy of digital full arch impressions were included. The primary outcome is the 3‐dimensional deviations between the study reference models. A risk of bias assessment was performed for clinical studies. A stratified meta‐analysis and a single‐armed meta‐analysis were conducted. Results A total of 49 studies were included, with 8 clinical studies and 41 in vitro studies. For comparison between IOS and conventional impressions, studies were categorized into two groups based on the different measurement methods employed: RMS and CMM. In studies using RMS, the result favored the IOS in the unparalleled situation with the mean difference of −99.29 μm (95% CI: [−141.38, −57.19], I2 = 81%), while the result was opposite with the mean difference of 13.62 μm (95% CI: [10.97, 16.28], I2 = 26%) when implants were paralleled. For different brands of IOS, the accuracy ranged from 76.11 μm (95% CI: [42.36, 109.86]) to 158.63 μm (95% CI: [−14.68, 331.93]). Conclusions Accuracy of intraoral scan is clinically acceptable in edentulous arches, especially for unparalleled implants. More clinical studies are needed to verify the present finding.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".