Accuracy of Photogrammetry, Intraoral Scanning, and Conventional Impression for Multiple Implants: An In Vitro Study
Bibliographic record
Abstract
OBJECTIVES: This in vitro study compared the accuracy of conventional impressions (CNVs), photogrammetry (PG), and intraoral scanning (IOS) for recording implant impressions of edentulous segments, ranging from part to complete arches by different evaluation methods. METHODS: The master model for an edentulous maxillary arch was created with six implants (a-f). CNVs, PG, and IOS were used for impressions. Three impression ranges (bcde, bcdef, and abcdef) were chosen for analysis. The best-fit algorithm, absolute linear deviation, and angular deviation were used for evaluation. Trueness and precision were analyzed by two-way ANOVA and the Kruskal-Wallis test, respectively. RESULTS: The accuracy of multiple implant impressions was significantly influenced by the impression method and impression range (p < 0.05) regardless of the evaluation methods used. At smaller ranges (bcde and bcdef), there was no difference in the trueness of the three impression methods, whereas at a larger range (abcdef), both PG and CNV exhibited similar trueness, which was significantly higher than that of IOS(p < 0.05). The precision of PG was significantly better than that of CNV and IOS in most of cases (p < 0.05). As the range expanded, the trueness and precision of PG and IOS decreased (p < 0.05), whereas the accuracy of CNV remained stable. CONCLUSIONS: In the case of large-range impressions, PG demonstrated a similar degree of trueness and better precision compared with CNVs, whereas the trueness and precision of the intraoral scanning were worse. This indicated that PG might be a promising method for multiple implant impressions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".