Calibrated intraoral scan protocol (<scp>CISP</scp>) for full‐arch implant impressions: An in vitro comparison to conventional impression, intraoral scan, and intraoral scan with scan‐aid
Bibliographic record
Abstract
OBJECTIVE: To assess a newly developed intraoral scan protocol in enhancing the accuracy of complete-arch implant impressions. MATERIALS AND METHODS: Four impression approaches were applied to the same maxillary edentulous model with 6 implants: (1) intraoral scan (IOS), (2) intraoral scan with scan aid (IOS-SA), (3) calibrated intraoral scan protocol (CISP), and (4) conventional splinted open-tray impression (CONV). Each approach was repeated 10 times, and a direct scan of the model with a desktop scanner was used as a reference model. The alignment of scans and the reference model was conducted by two methods: (a) aligning all scan bodies to evaluate the overall fit, and (b) aligning the first and second scan bodies to simulate the Sheffield fit test for passive fitting of multiple implant-supported prostheses. Linear deviations from the reference model (trueness) and within each group (precision) were analyzed using Python scripts. RESULTS: When aligned by all scan bodies, the CISP group exhibited comparable mean trueness (38.33 μm) and precision (45.97 μm) to the CONV group (44.30 and 47.92 μm respectively), both of which significantly outperformed the IOS group (86.82 and 83.17 μm, respectively). Furthermore, in the virtual Sheffield fit test, the CISP group achieved the highest levels of mean trueness at the end span (121.7 μm), making a linear deviation reduction of 36.7%, 60%, and 41.4% when compared to the CONV, the IOS, and the IOS-SA groups, respectively. Moreover, the CISP group (104.3 μm) displayed a remarkable 65, 182, and 86 μm advantage in precision over the CONV, IOS, and IOS-SA groups, respectively. CONCLUSION: CISP demonstrated comparable accuracy to the gold standard, the conventional splinted open-tray impression. Furthermore, it excelled in the virtual passive fitting test.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".