Use of the universal scan template to achieve a predictable optical impression: Preliminary data of a case series study in complete edentulous patients
Bibliographic record
Abstract
BACKGROUND: Full-arch IOS scan of edentulous areas rehabilitated with dental implants is nowadays still described as an unpredictable procedure. To improve the accuracy, a universal scan template (UST®) is proposed in this article. The clinician can easily assemble the template with a mechanical coupling, by matching the scan bodies with objects of known dimension characterized by specific markers. The UST® facilitates the scanning of an entire arch on scan bodies, reducing the learning curve, simplifying acquisition movements, shortening the scanning time, and drastically reducing the risk of distortions and aberrations of the scans. MATERIALS AND METHODS: In a case series study on 12 patients, the improvement in the accuracy of the scans with UST® was validated by comparing the STL files derived from scans with and without the guide in place. A titanium bar was produced from each optical impression. RESULTS: The bars obtained from the optical impressions taken without UST® were found to be nonpassive in the mouth in the majority of the cases. On the contrary with the use of UST® we obtained 12 passive prosthetic rehabilitations. CONCLUSIONS: The proposed solution may represent a valid method to improve the predictability of full arch optical impressions on implants.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".